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In artificial intelligence, a problem-solving agent refers to a type of intelligent agent designed to address and solve complex problems or tasks in its environment. These agents are a fundamental concept in AI and are used in various applications, from game-playing algorithms to robotics and decision-making systems. Here are some key characteristics and components of a problem-solving agent:
Problem-solving agents can vary greatly in complexity, from simple algorithms that solve straightforward puzzles to highly sophisticated AI systems that tackle complex, real-world problems. The design and implementation of problem-solving agents depend on the specific problem domain and the goals of the AI application.
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Course info.
Lecture 2: reasoning: goal trees and problem solving.
Description: This lecture covers a symbolic integration program from the early days of AI. We use safe and heuristic transformations to simplify the problem, and then consider broader questions of how much knowledge is involved, and how the knowledge is represented.
Instructor: Patrick H. Winston
By adam zewe.
December 5, 2023 | MIT News
While Santa Claus may have a magical sleigh and nine plucky reindeer to help him deliver presents, for companies like FedEx, the optimization problem of efficiently routing holiday packages is so complicated that they often employ specialized software to find a solution.
This software, called a mixed-integer linear programming (MILP) solver, splits a massive optimization problem into smaller pieces and uses generic algorithms to try and find the best solution. However, the solver could take hours — or even days — to arrive at a solution.
The process is so onerous that a company often must stop the software partway through, accepting a solution that is not ideal but the best that could be generated in a set amount of time.
Researchers from MIT and ETH Zurich used machine learning to speed things up.
They identified a key intermediate step in MILP solvers that has so many potential solutions it takes an enormous amount of time to unravel, which slows the entire process. The researchers employed a filtering technique to simplify this step, then used machine learning to find the optimal solution for a specific type of problem.
Their data-driven approach enables a company to use its own data to tailor a general-purpose MILP solver to the problem at hand.
This new technique sped up MILP solvers between 30 and 70 percent, without any drop in accuracy. One could use this method to obtain an optimal solution more quickly or, for especially complex problems, a better solution in a tractable amount of time.
This approach could be used wherever MILP solvers are employed, such as by ride-hailing services, electric grid operators, vaccination distributors, or any entity faced with a thorny resource-allocation problem.
“Sometimes, in a field like optimization, it is very common for folks to think of solutions as either purely machine learning or purely classical. I am a firm believer that we want to get the best of both worlds, and this is a really strong instantiation of that hybrid approach,” says senior author Cathy Wu, the Gilbert W. Winslow Career Development Assistant Professor in Civil and Environmental Engineering (CEE), and a member of a member of the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society (IDSS).
Wu wrote the paper with co-lead authors Sirui Li, an IDSS graduate student, and Wenbin Ouyang, a CEE graduate student; as well as Max Paulus, a graduate student at ETH Zurich. The research will be presented at the Conference on Neural Information Processing Systems.
Tough to solve
MILP problems have an exponential number of potential solutions. For instance, say a traveling salesperson wants to find the shortest path to visit several cities and then return to their city of origin. If there are many cities which could be visited in any order, the number of potential solutions might be greater than the number of atoms in the universe.
“These problems are called NP-hard, which means it is very unlikely there is an efficient algorithm to solve them. When the problem is big enough, we can only hope to achieve some suboptimal performance,” Wu explains.
An MILP solver employs an array of techniques and practical tricks that can achieve reasonable solutions in a tractable amount of time.
A typical solver uses a divide-and-conquer approach, first splitting the space of potential solutions into smaller pieces with a technique called branching. Then, the solver employs a technique called cutting to tighten up these smaller pieces so they can be searched faster.
Cutting uses a set of rules that tighten the search space without removing any feasible solutions. These rules are generated by a few dozen algorithms, known as separators, that have been created for different kinds of MILP problems.
Wu and her team found that the process of identifying the ideal combination of separator algorithms to use is, in itself, a problem with an exponential number of solutions.
“Separator management is a core part of every solver, but this is an underappreciated aspect of the problem space. One of the contributions of this work is identifying the problem of separator management as a machine learning task to begin with,” she says.
Shrinking the solution space
She and her collaborators devised a filtering mechanism that reduces this separator search space from more than 130,000 potential combinations to around 20 options. This filtering mechanism draws on the principle of diminishing marginal returns, which says that the most benefit would come from a small set of algorithms, and adding additional algorithms won’t bring much extra improvement.
Then they use a machine-learning model to pick the best combination of algorithms from among the 20 remaining options.
This model is trained with a dataset specific to the user’s optimization problem, so it learns to choose algorithms that best suit the user’s particular task. Since a company like FedEx has solved routing problems many times before, using real data gleaned from past experience should lead to better solutions than starting from scratch each time.
The model’s iterative learning process, known as contextual bandits, a form of reinforcement learning, involves picking a potential solution, getting feedback on how good it was, and then trying again to find a better solution.
This data-driven approach accelerated MILP solvers between 30 and 70 percent without any drop in accuracy. Moreover, the speedup was similar when they applied it to a simpler, open-source solver and a more powerful, commercial solver.
In the future, Wu and her collaborators want to apply this approach to even more complex MILP problems, where gathering labeled data to train the model could be especially challenging. Perhaps they can train the model on a smaller dataset and then tweak it to tackle a much larger optimization problem, she says. The researchers are also interested in interpreting the learned model to better understand the effectiveness of different separator algorithms.
This research is supported, in part, by Mathworks, the National Science Foundation (NSF), the MIT Amazon Science Hub, and MIT’s Research Support Committee.
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Conference is exploring burgeoning connections between the two fields.
Traditionally, mathematicians jot down their formulas using paper and pencil, seeking out what they call pure and elegant solutions. In the 1970s, they hesitantly began turning to computers to assist with some of their problems. Decades later, computers are often used to crack the hardest math puzzles. Now, in a similar vein, some mathematicians are turning to machine learning tools to aid in their numerical pursuits.
“Mathematicians are beginning to embrace machine learning,” says Sergei Gukov, the John D. MacArthur Professor of Theoretical Physics and Mathematics at Caltech, who put together the Mathematics and Machine Learning 2023 conference, which is taking place at Caltech December 10–13.
“There are some mathematicians who may still be skeptical about using the tools,” Gukov says. “The tools are mischievous and not as pure as using paper and pencil, but they work.”
Machine learning is a subfield of AI, or artificial intelligence , in which a computer program is trained on large datasets and learns to find new patterns and make predictions. The conference, the first put on by the new Richard N. Merkin Center for Pure and Applied Mathematics, will help bridge the gap between developers of machine learning tools (the data scientists) and the mathematicians. The goal is to discuss ways in which the two fields can complement each other.
“It’s a two-way street,” says Gukov, who is the director of the new Merkin Center, which was established by Caltech Trustee Richard Merkin.
“Mathematicians can help come up with clever new algorithms for machine learning tools like the ones used in generative AI programs like ChatGPT, while machine learning can help us crack difficult math problems.”
Yi Ni, a professor of mathematics at Caltech, plans to attend the conference, though he says he does not use machine learning in his own research, which involves the field of topology and, specifically, the study of mathematical knots in lower dimensions. “Some mathematicians are more familiar with these advanced tools than others,” Ni says. “You need to know somebody who is an expert in machine learning and willing to help. Ultimately, I think AI for math will become a subfield of math.”
One tough problem that may unravel with the help of machine learning, according to Gukov, is known as the Riemann hypothesis. Named after the 19th-century mathematician Bernhard Riemann, this problem is one of seven Millennium Problems selected by the Clay Mathematics Institute; a $1 million prize will be awarded for the solution to each problem.
The Riemann hypothesis centers around a formula known as the Riemann zeta function, which packages information about prime numbers. If proved true, the hypothesis would provide a new understanding of how prime numbers are distributed. Machine learning tools could help crack the problem by providing a new way to run through more possible iterations of the problem.
“Machine learning tools are very good at recognizing patterns and analyzing very complex problems,” Gukov says.
Ni agrees that machine learning can serve as a helpful assistant. “Machine learning solutions may not be as beautiful, but they can find new connections,” he says. “But you still need a mathematician to turn the questions into something computers can solve.”
Gukov has used machine learning himself to untangle problems in knot theory. Knot theory is the study of abstract knots, which are similar to the knots you might find on a shoestring, but the ends of the strings are closed into loops. These mathematical knots can be entwined in various ways, and mathematicians like Gukov want to understand their structures and how they relate to each other. The work has relationships to other fields of mathematics such as representation theory and quantum algebra, and even quantum physics.
In particular, Gukov and his colleagues are working to solve what is called the smooth Poincaré conjecture in four dimensions. The original Poincaré conjecture, which is also a Millennium Problem, was proposed by mathematician Henri Poincaré early in the 20th century. It was ultimately solved from 2002 to 2003 by Grigori Perelman (who famously turned down his prize of $1 million). The problem involves comparing spheres to certain types of manifolds that look like spheres; manifolds are shapes that are projections of higher-dimensional objects onto lower dimensions. Gukov says the problem is like asking, “Are objects that look like spheres really spheres?”
The four-dimensional smooth Poincaré conjecture holds that, in four dimensions, all manifolds that look like spheres are indeed actually spheres. In an attempt to solve this conjecture, Gukov and his team develop a machine learning approach to evaluate so-called ribbon knots.
“Our brain cannot handle four dimensions, so we package shapes into knots,” Gukov says. “A ribbon is where the string in a knot pierces through a different part of the string in three dimensions but doesn’t pierce through anything in four dimensions. Machine learning lets us analyze the ‘ribboness’ of knots, a yes-or-no property of knots that has applications to the smooth Poincaré conjecture.”
“This is where machine learning comes to the rescue,” writes Gukov and his team in a preprint paper titled “ Searching for Ribbons with Machine Learning .” “It has the ability to quickly search through many potential solutions and, more importantly, to improve the search based on the successful ‘games’ it plays. We use the word ‘games’ since the same types of algorithms and architectures can be employed to play complex board games, such as Go or chess, where the goals and winning strategies are similar to those in math problems.”
On the flip side, math can help in developing machine learning algorithms, Gukov explains. A mathematical mindset, he says, can bring fresh ideas to the development of the algorithms behind AI tools. He cites Peter Shor as an example of a mathematician who brought insight to computer science problems. Shor, who graduated from Caltech with a bachelor’s degree in mathematics in 1981, famously came up with what is known as Shor’s algorithm, a set of rules that could allow quantum computers of the future to factor integers faster than typical computers, thereby breaking digital encryption codes.
Today’s machine learning algorithms are trained on large sets of data. They churn through mountains of data on language, images, and more to recognize patterns and come up with new connections. However, data scientists don’t always know how the programs reach their conclusions. The inner workings are hidden in a so-called “black box.” A mathematical approach to developing the algorithms would reveal what’s happening “under the hood,” as Gukov says, leading to a deeper understanding of how the algorithms work and thus can be improved.
“Math,” says Gukov, “is fertile ground for new ideas.”
The conference will take place at the Merkin Center on the eighth floor of Caltech Hall.
Breaking the 21-day myth: machine learning unlocks the secrets of habit formation, conventional computers can learn to solve tricky quantum problems in physics and chemistry, ai algorithm predicts future crimes one week in advance with 90% accuracy, ai reveals unsuspected connections hidden in the complex math underlying search for exoplanets, seeing quadruple: artificial intelligence leads to discovery that can help solve cosmological puzzles, “friends and strangers” theorem – math professor makes breakthrough in ramsey numbers, the fractal dimension of the us zip code system: 1.78, mathematician claims breakthrough in the sudoku problem, mathematics and lego: the deeper meaning of combined systems and networks.
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Problems in Artificial Intelligence (AI) come in different forms, each with its own set of challenges and potential for innovation. From image recognition to natural language processing, AI problems exhibit distinct characteristics that shape the strategies and techniques used to tackle them effectively. In this article, we delve into the fundamental characteristics of AI problems, providing light on what makes them so fascinating and formidable.
Table of Content
Addressing the challenges of ai problems, examples of ai applications and challenges across domains, characteristics of artificial intelligence problems – faqs.
Before exploring the characteristics, let’s clarify some essential AI concepts:
By understanding these key terminologies, we can better grasp the characteristics of AI problems and the techniques used to address them. These concepts form the foundation of AI problem-solving and provide the framework for developing innovative solutions to real-world challenges.
Let’s explore the core characteristics that differentiate AI problems:
These characteristics collectively shape the challenges and opportunities involved in developing and deploying AI systems across various domains and applications.
The characteristics of AI problems present unique challenges that require innovative approaches to solution development. Some of the key aspects to consider in tackling these challenges include:
By addressing these challenges through innovative methodologies and interdisciplinary collaboration, we can harness the full potential of AI to solve complex problems and drive societal progress.
Problem: A delivery robot navigating a busy warehouse to locate and retrieve a specific item.
Characteristics:
Problem: A sentiment analysis system in NLP classifying customer reviews as positive, negative, or neutral.
Problem: A medical image recognition system in Computer Vision designed to detect tumors in X-rays or MRI scans.
The premises of AI-based problems – complexity, uncertainty, subjectivity, and more, – bring an unavoidable difficulty to the table. These features must be known for building appropriate AI because this is necessary. Through the use of machine learning, probabilistic reasoning, and knowledge representation which are referred to as the tools in AI development alongside the ethical considerations, these designers and scientists can face such complexities well and give shape to AI in a way that will be beneficial to society.
The core characteristics of AI problems include complexity, uncertainty and ambiguity, lack of clear problem definition, non-linearity, dynamism, subjectivity, interactivity, context sensitivity, and ethical considerations.
Problem-solving in AI involves creating algorithms and methods that enable machines to imitate human capabilities of logical and reasonable thinking in certain situations.
Search space refers to the area where an agent involved in the problem-solving process can examine all the possible states or settings with the hope of discovering a solution.
AI algorithms are designed to handle unclear circumstances and make decisions based on imperfect data or noisy information.
Examples include robotics (e.g., delivery robots navigating busy warehouses), natural language processing (e.g., sentiment analysis of customer reviews), and computer vision (e.g., medical image recognition for detecting tumors).
Ethical considerations are crucial in AI development to address issues such as bias, justice, privacy, and responsibility, ensuring that AI technologies are deployed responsibly and ethically.
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Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans , such as the ability to reason. Although there are as yet no AIs that match full human flexibility over wider domains or in tasks requiring much everyday knowledge, some AIs perform specific tasks as well as humans. Learn more.
No, artificial intelligence and machine learning are not the same, but they are closely related. Machine learning is the method to train a computer to learn from its inputs but without explicit programming for every circumstance. Machine learning helps a computer to achieve artificial intelligence.
artificial intelligence (AI) , the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since their development in the 1940s, digital computers have been programmed to carry out very complex tasks—such as discovering proofs for mathematical theorems or playing chess —with great proficiency. Despite continuing advances in computer processing speed and memory capacity, there are as yet no programs that can match full human flexibility over wider domains or in tasks requiring much everyday knowledge. On the other hand, some programs have attained the performance levels of human experts and professionals in executing certain specific tasks, so that artificial intelligence in this limited sense is found in applications as diverse as medical diagnosis , computer search engines , voice or handwriting recognition, and chatbots .
Artificial intelligence is changing how we interact online, how we manage our finances, and even how we work. Learn more with Britannica Money .
All but the simplest human behavior is ascribed to intelligence, while even the most complicated insect behavior is usually not taken as an indication of intelligence. What is the difference? Consider the behavior of the digger wasp , Sphex ichneumoneus . When the female wasp returns to her burrow with food, she first deposits it on the threshold , checks for intruders inside her burrow, and only then, if the coast is clear, carries her food inside. The real nature of the wasp’s instinctual behavior is revealed if the food is moved a few inches away from the entrance to her burrow while she is inside: on emerging, she will repeat the whole procedure as often as the food is displaced. Intelligence—conspicuously absent in the case of the wasp—must include the ability to adapt to new circumstances.
(Read Ray Kurzweil’s Britannica essay on the future of “Nonbiological Man.”)
Psychologists generally characterize human intelligence not by just one trait but by the combination of many diverse abilities. Research in AI has focused chiefly on the following components of intelligence: learning, reasoning, problem solving , perception , and using language.
There are a number of different forms of learning as applied to artificial intelligence. The simplest is learning by trial and error. For example, a simple computer program for solving mate-in-one chess problems might try moves at random until mate is found. The program might then store the solution with the position so that, the next time the computer encountered the same position, it would recall the solution. This simple memorizing of individual items and procedures—known as rote learning—is relatively easy to implement on a computer. More challenging is the problem of implementing what is called generalization . Generalization involves applying past experience to analogous new situations. For example, a program that learns the past tense of regular English verbs by rote will not be able to produce the past tense of a word such as jump unless the program was previously presented with jumped , whereas a program that is able to generalize can learn the “add -ed ” rule for regular verbs ending in a consonant and so form the past tense of jump on the basis of experience with similar verbs.
(Read Yuval Noah Harari’s Britannica essay on the future of “Nonconscious Man.”)
Not every challenge requires an algorithmic approach.
AI is increasingly informing business decisions but can be misused if executives stick with old decision-making styles. A key to effective collaboration is to recognize which parts of a problem to hand off to the AI and which the managerial mind will be better at solving. While AI is superior at data-intensive prediction problems, humans are uniquely suited to the creative thought experiments that underpin the best decisions.
Business leaders often pride themselves on their intuitive decision-making. They didn’t get to be division heads and CEOs by robotically following some leadership checklist. Of course, intuition and instinct can be important leadership tools, but not if they’re indiscriminately applied.
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According to cognitive psychology and related disciplines, the development of complex problem-solving behaviour in biological agents depends on hierarchical cognitive mechanisms. Hierarchical reinforcement learning is a promising computational approach that may eventually yield comparable problem-solving behaviour in artificial agents and robots. However, so far, the problem-solving abilities of many human and non-human animals are clearly superior to those of artificial systems. Here we propose steps to integrate biologically inspired hierarchical mechanisms to enable advanced problem-solving skills in artificial agents. We first review the literature in cognitive psychology to highlight the importance of compositional abstraction and predictive processing. Then we relate the gained insights with contemporary hierarchical reinforcement learning methods. Interestingly, our results suggest that all identified cognitive mechanisms have been implemented individually in isolated computational architectures, raising the question of why there exists no single unifying architecture that integrates them. As our final contribution, we address this question by providing an integrative perspective on the computational challenges to develop such a unifying architecture. We expect our results to guide the development of more sophisticated cognitively inspired hierarchical machine learning architectures.
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Gruber, R. et al. New Caledonian crows use mental representations to solve metatool problems. Curr. Biol. 29 , 686–692 (2019).
Article Google Scholar
Butz, M. V. & Kutter, E. F. How the Mind Comes into Being (Oxford Univ. Press, 2017).
Perkins, D. N. & Salomon, G. in International Encyclopedia of Education (eds. Husen T. & Postelwhite T. N.) 6452–6457 (Pergamon Press, 1992).
Botvinick, M. M., Niv, Y. & Barto, A. C. Hierarchically organized behavior and its neural foundations: a reinforcement learning perspective. Cognition 113 , 262–280 (2009).
Tomov, M. S., Yagati, S., Kumar, A., Yang, W. & Gershman, S. J. Discovery of hierarchical representations for efficient planning. PLoS Comput. Biol. 16 , e1007594 (2020).
Arulkumaran, K., Deisenroth, M. P., Brundage, M. & Bharath, A. A. Deep reinforcement learning: a brief survey. IEEE Signal Process. Mag. 34 , 26–38 (2017).
Li, Y. Deep reinforcement learning: an overview. Preprint at https://arxiv.org/abs/1701.07274 (2018).
Sutton, R. S. & Barto, A. G. Reinforcement Learning : An Introduction 2nd edn (MIT Press, 2018).
Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nat. Mach. Intell. 1 , 133–143 (2019).
Eppe, M., Nguyen, P. D. H. & Wermter, S. From semantics to execution: integrating action planning with reinforcement learning for robotic causal problem-solving. Front. Robot. AI 6 , 123 (2019).
Oh, J., Singh, S., Lee, H. & Kohli, P. Zero-shot task generalization with multi-task deep reinforcement learning. In Proc. 34th International Conference on Machine Learning ( ICML ) (eds. Precup, D. & Teh, Y. W.) 2661–2670 (PMLR, 2017).
Sohn, S., Oh, J. & Lee, H. Hierarchical reinforcement learning for zero-shot generalization with subtask dependencies. In Proc. 32nd International Conference on Neural Information Processing Systems ( NeurIPS ) (eds Bengio S. et al.) Vol. 31, 7156–7166 (ACM, 2018).
Hegarty, M. Mechanical reasoning by mental simulation. Trends Cogn. Sci. 8 , 280–285 (2004).
Klauer, K. J. Teaching for analogical transfer as a means of improving problem-solving, thinking and learning. Instruct. Sci. 18 , 179–192 (1989).
Duncker, K. & Lees, L. S. On problem-solving. Psychol. Monographs 58, No.5 (whole No. 270), 85–101 https://doi.org/10.1037/h0093599 (1945).
Dayan, P. Goal-directed control and its antipodes. Neural Netw. 22 , 213–219 (2009).
Dolan, R. J. & Dayan, P. Goals and habits in the brain. Neuron 80 , 312–325 (2013).
O’Doherty, J. P., Cockburn, J. & Pauli, W. M. Learning, reward, and decision making. Annu. Rev. Psychol. 68 , 73–100 (2017).
Tolman, E. C. & Honzik, C. H. Introduction and removal of reward, and maze performance in rats. Univ. California Publ. Psychol. 4 , 257–275 (1930).
Google Scholar
Butz, M. V. & Hoffmann, J. Anticipations control behavior: animal behavior in an anticipatory learning classifier system. Adaptive Behav. 10 , 75–96 (2002).
Miller, G. A., Galanter, E. & Pribram, K. H. Plans and the Structure of Behavior (Holt, Rinehart & Winston, 1960).
Botvinick, M. & Weinstein, A. Model-based hierarchical reinforcement learning and human action control. Philos. Trans. R. Soc. B Biol. Sci. 369 , 20130480 (2014).
Wiener, J. M. & Mallot, H. A. ’Fine-to-coarse’ route planning and navigation in regionalized environments. Spatial Cogn. Comput. 3 , 331–358 (2003).
Stock, A. & Stock, C. A short history of ideo-motor action. Psychol. Res. 68 , 176–188 (2004).
Hommel, B., Müsseler, J., Aschersleben, G. & Prinz, W. The theory of event coding (TEC): a framework for perception and action planning. Behav. Brain Sci. 24 , 849–878 (2001).
Hoffmann, J. in Anticipatory Behavior in Adaptive Learning Systems : Foundations , Theories and Systems (eds Butz, M. V. et al.) 44–65 (Springer, 2003).
Kunde, W., Elsner, K. & Kiesel, A. No anticipation-no action: the role of anticipation in action and perception. Cogn. Process. 8 , 71–78 (2007).
Barsalou, L. W. Grounded cognition. Annu. Rev. Psychol. 59 , 617–645 (2008).
Butz, M. V. Toward a unified sub-symbolic computational theory of cognition. Front. Psychol. 7 , 925 (2016).
Pulvermüller, F. Brain embodiment of syntax and grammar: discrete combinatorial mechanisms spelt out in neuronal circuits. Brain Lang. 112 , 167–179 (2010).
Sutton, R. S., Precup, D. & Singh, S. Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning. Artif. Intell. 112 , 181–211 (1999).
Article MathSciNet MATH Google Scholar
Flash, T. & Hochner, B. Motor primitives in vertebrates and invertebrates. Curr. Opin. Neurobiol. 15 , 660–666 (2005).
Schaal, S. in Adaptive Motion of Animals and Machines (eds. Kimura, H. et al.) 261–280 (Springer, 2006).
Feldman, J., Dodge, E. & Bryant, J. in The Oxford Handbook of Linguistic Analysis (eds Heine, B. & Narrog, H.) 111–138 (Oxford Univ. Press, 2009).
Fodor, J. A. Language, thought and compositionality. Mind Lang. 16 , 1–15 (2001).
Frankland, S. M. & Greene, J. D. Concepts and compositionality: in search of the brain’s language of thought. Annu. Rev. Psychol. 71 , 273–303 (2020).
Hummel, J. E. Getting symbols out of a neural architecture. Connection Sci. 23 , 109–118 (2011).
Haynes, J. D., Wisniewski, D., Gorgen, K., Momennejad, I. & Reverberi, C. FMRI decoding of intentions: compositionality, hierarchy and prospective memory. In Proc. 3rd International Winter Conference on Brain-Computer Interface ( BCI ), 1-3 (IEEE, 2015).
Gärdenfors, P. The Geometry of Meaning : Semantics Based on Conceptual Spaces (MIT Press, 2014).
Book MATH Google Scholar
Lakoff, G. & Johnson, M. Philosophy in the Flesh (Basic Books, 1999).
Eppe, M. et al. A computational framework for concept blending. Artif. Intell. 256 , 105–129 (2018).
Turner, M. The Origin of Ideas (Oxford Univ. Press, 2014).
Deci, E. L. & Ryan, R. M. Self-determination theory and the facilitation of intrinsic motivation. Am. Psychol. 55 , 68–78 (2000).
Friston, K. et al. Active inference and epistemic value. Cogn. Neurosci. 6 , 187–214 (2015).
Berlyne, D. E. Curiosity and exploration. Science 153 , 25–33 (1966).
Loewenstein, G. The psychology of curiosity: a review and reinterpretation. Psychol. Bull. 116 , 75–98 (1994).
Oudeyer, P.-Y., Kaplan, F. & Hafner, V. V. Intrinsic motivation systems for autonomous mental development. In IEEE Transactions on Evolutionary Computation (eds. Coello, C. A. C. et al.) Vol. 11, 265–286 (IEEE, 2007).
Pisula, W. Play and exploration in animals—a comparative analysis. Polish Psychol. Bull. 39 , 104–107 (2008).
Jeannerod, M. Mental imagery in the motor context. Neuropsychologia 33 , 1419–1432 (1995).
Kahnemann, D. & Tversky, A. in Judgement under Uncertainty : Heuristics and Biases (eds Kahneman, D. et al.) Ch. 14, 201–208 (Cambridge Univ. Press, 1982).
Wells, G. L. & Gavanski, I. Mental simulation of causality. J. Personal. Social Psychol. 56 , 161–169 (1989).
Taylor, S. E., Pham, L. B., Rivkin, I. D. & Armor, D. A. Harnessing the imagination: mental simulation, self-regulation and coping. Am. Psychol. 53 , 429–439 (1998).
Kaplan, F. & Oudeyer, P.-Y. in Embodied Artificial Intelligence , Lecture Notes in Computer Science Vol. 3139 (eds Iida, F. et al.) 259–270 (Springer, 2004).
Schmidhuber, J. Formal theory of creativity, fun, and intrinsic motivation. IEEE Trans. Auton. Mental Dev. 2 , 230–247 (2010).
Friston, K., Mattout, J. & Kilner, J. Action understanding and active inference. Biol. Cybern. 104 , 137–160 (2011).
Oudeyer, P.-Y. Computational theories of curiosity-driven learning. In The New Science of Curiosity (ed. Goren Gordon), 43-72 (Nova Science Publishers, 2018); https://arxiv.org/abs/1802.10546
Colombo, M. & Wright, C. First principles in the life sciences: the free-energy principle, organicism and mechanism. Synthese 198 , 3463–3488 (2021).
Article MathSciNet Google Scholar
Huang, Y. & Rao, R. P. Predictive coding. WIREs Cogn. Sci. 2 , 580–593 (2011).
Friston, K. The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11 , 127–138 (2010).
Knill, D. C. & Pouget, A. The Bayesian brain: the role of uncertainty in neural coding and computation. Trends Neurosci. 27 , 712–719 (2004).
Clark, A. Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci. 36 , 181–204 (2013).
Clark, A. Surfing Uncertainty : Prediction , Action and the Embodied Mind (Oxford Univ. Press, 2016).
Zacks, J. M., Speer, N. K., Swallow, K. M., Braver, T. S. & Reyonolds, J. R. Event perception: a mind/brain perspective. Psychol. Bull. 133 , 273–293 (2007).
Eysenbach, B., Ibarz, J., Gupta, A. & Levine, S. Diversity is all you need: learning skills without a reward function. In International Conference on Learning Representations (ICLR, 2019).
Frans, K., Ho, J., Chen, X., Abbeel, P. & Schulman, J. Meta learning shared hierarchies. In Proc. International Conference on Learning Representations https://openreview.net/pdf?id=SyX0IeWAW (ICLR, 2018).
Heess, N. et al. Learning and transfer of modulated locomotor controllers. Preprint at https://arxiv.org/abs/1610.05182 (2016).
Jiang, Y., Gu, S., Murphy, K. & Finn, C. Language as an abstraction for hierarchical deep reinforcement learning. In Neural Information Processing Systems ( NeurIPS ) (eds. Wallach, H. et al.) 9414–9426 (ACM, 2019).
Li, A. C., Florensa, C., Clavera, I. & Abbeel, P. Sub-policy adaptation for hierarchical reinforcement learning. In Proc. International Conference on Learning Representations https://openreview.net/forum?id=ByeWogStDS (ICLR, 2020).
Qureshi, A. H. et al. Composing task-agnostic policies with deep reinforcement learning. In Proc. International Conference on Learning Representations https://openreview.net/forum?id=H1ezFREtwH (ICLR, 2020).
Sharma, A., Gu, S., Levine, S., Kumar, V. & Hausman, K. Dynamics-aware unsupervised discovery of skills. In Proc. International Conference on Learning Representations https://openreview.net/forum?id=HJgLZR4KvH (ICLR, 2020).
Tessler, C., Givony, S., Zahavy, T., Mankowitz, D. J. & Mannor, S. A deep hierarchical approach to lifelong learning in minecraft. In Proc. 31st AAAI Conference on Artificial Intelligence 1553–1561 (AAAI, 2017).
Vezhnevets, A. et al. Strategic attentive writer for learning macro-actions. In Neural Information Processing Systems ( NIPS ) (eds. Lee, D. et al.) 3494–3502 (NIPS, 2016).
Devin, C., Gupta, A., Darrell, T., Abbeel, P. & Levine, S. Learning modular neural network policies for multi-task and multi-robot transfer. In Proc. International Conference on Robotics and Automation ( ICRA ) (eds. Okamura, A. et al.) 2169–2176 (IEEE, 2017).
Hejna, D. J., Abbeel, P. & Pinto, L. Hierarchically decoupled morphological transfer. In Proc. International Conference on Machine Learning ( ICML ) (eds. Daumé III, H. & Singh, A.) 11409–11420 (PMLR, 2020).
Hamrick, J. B. et al. On the role of planning in model-based deep reinforcement learning. In Proc. International Conference on Learning Representations https://openreview.net/pdf?id=IrM64DGB21 (ICLR, 2021).
Sutton, R. S. Integrated architectures for learning, planning, and reacting based on approximating dynamic programming. In Proc. 7th International Conference on Machine Learning ( ICML ) (eds. Porter, B. W. & Mooney, R. J.) 216–224 (Morgan Kaufmann, 1990).
Nau, D. et al. SHOP2: an HTN planning system. J. Artif. Intell. Res. 20 , 379–404 (2003).
Article MATH Google Scholar
Lyu, D., Yang, F., Liu, B. & Gustafson, S. SDRL: interpretable and data-efficient deep reinforcement learning leveraging symbolic planning. In Proc. AAAI Conference on Artificial Intelligence Vol. 33, 2970–2977 (AAAI, 2019).
Ma, A., Ouimet, M. & Cortés, J. Hierarchical reinforcement learning via dynamic subspace search for multi-agent planning. Auton. Robot. 44 , 485–503 (2020).
Bacon, P.-L., Harb, J. & Precup, D. The option-critic architecture. In Proc. 31st AAAI Conference on Artificial Intelligence 1726–1734 (AAAI, 2017).
Dietterich, T. G. State abstraction in MAXQ hierarchical reinforcement learning. In Advances in Neural Information Processing Systems ( NIPS ) (eds. Solla, S. et al.) Vol. 12, 994–1000 (NIPS, 1999).
Kulkarni, T. D., Narasimhan, K. R., Saeedi, A. & Tenenbaum, J. B. Hierarchical deep reinforcement learning: integrating temporal abstraction and intrinsic motivation. In Neural Information Processing Systems ( NIPS ) (eds. Lee, D. et al.) 3675–3683 (NIPS, 2016).
Shankar, T., Pinto, L., Tulsiani, S. & Gupta, A. Discovering motor programs by recomposing demonstrations. In Proc. International Conference on Learning Representations https://openreview.net/attachment?id=rkgHY0NYwr&name=original_pdf (ICLR, 2020).
Vezhnevets, A. S., Wu, Y. T., Eckstein, M., Leblond, R. & Leibo, J. Z. Options as responses: grounding behavioural hierarchies in multi-agent reinforcement learning. In Proc. International Conference on Machine Learning ( ICML ) (eds. Daumé III, H. & Singh, A.) 9733–9742 (PMLR, 2020).
Ghazanfari, B., Afghah, F. & Taylor, M. E. Sequential association rule mining for autonomously extracting hierarchical task structures in reinforcement learning. IEEE Access 8 , 11782–11799 (2020).
Levy, A., Konidaris, G., Platt, R. & Saenko, K. Learning multi-level hierarchies with hindsight. In Proc. International Conference on Learning Representations https://openreview.net/pdf?id=ryzECoAcY7 (ICLR, 2019).
Nachum, O., Gu, S., Lee, H. & Levine, S. Data-efficient hierarchical reinforcement learning. In Proc. 32nd International Conference on Neural Information Processing Systems (NIPS) (eds. Bengio, S. et al.) 3307–3317 (NIPS, 2018).
Rafati, J. & Noelle, D. C. Learning representations in model-free hierarchical reinforcement learning. In Proc. 33rd AAAI Conference on Artificial Intelligence 10009–10010 (AAAI, 2019).
Röder, F., Eppe, M., Nguyen, P. D. H. & Wermter, S. Curious hierarchical actor-critic reinforcement learning. In Proc. International Conference on Artificial Neural Networks ( ICANN ) (eds. Farkaš, I. et al.) 408–419 (Springer, 2020).
Zhang, T., Guo, S., Tan, T., Hu, X. & Chen, F. Generating adjacency-constrained subgoals in hierarchical reinforcement learning. In Neural Information Processing Systems ( NIPS ) (eds. Larochelle, H. et al.) 21579-21590 (NIPS, 2020).
Lample, G. & Chaplot, D. S. Playing FPS games with deep reinforcement learning. In Proc. 31st AAAI Conference on Artificial Intelligence 2140–2146 (AAAI, 2017).
Vezhnevets, A. S. et al. FeUdal networks for hierarchical reinforcement learning. In Proc. 34th International Conference on Machine Learning ( ICML ) (eds. Precup, D. & Teh, Y. W.) Vol. 70, 3540–3549 (PMLR, 2017).
Wulfmeier, M. et al. Compositional Transfer in Hierarchical Reinforcement Learning. In Robotics: Science and System XVI (RSS) (eds. Toussaint M. et al.) (Robotics: Science and Systems Foundation, 2020); https://arxiv.org/abs/1906.11228
Yang, Z., Merrick, K., Jin, L. & Abbass, H. A. Hierarchical deep reinforcement learning for continuous action control. IEEE Trans. Neural Netw. Learn. Syst. 29 , 5174–5184 (2018).
Toussaint, M., Allen, K. R., Smith, K. A. & Tenenbaum, J. B. Differentiable physics and stable modes for tool-use and manipulation planning. In Proc. Robotics : Science and Systems XIV ( RSS ) (eds. Kress-Gazit, H. et al.) https://ipvs.informatik.uni-stuttgart.de/mlr/papers/18-toussaint-RSS.pdf (Robotics: Science and Systems Foundation, 2018).
Akrour, R., Veiga, F., Peters, J. & Neumann, G. Regularizing reinforcement learning with state abstraction. In Proc. IEEE / RSJ International Conference on Intelligent Robots and Systems ( IROS ) 534–539 (IEEE, 2018).
Schaul, T. & Ring, M. Better generalization with forecasts. In Proc. 23rd International Joint Conference on Artificial Intelligence ( IJCAI ) (ed. Rossi, F.) 1656–1662 (AAAI, 2013).
Colas, C., Akakzia, A., Oudeyer, P.-Y., Chetouani, M. & Sigaud, O. Language-conditioned goal generation: a new approach to language grounding for RL. Preprint at https://arxiv.org/abs/2006.07043 (2020).
Blaes, S., Pogancic, M. V., Zhu, J. J. & Martius, G. Control what you can: intrinsically motivated task-planning agent. Neural Inf. Process. Syst. 32 , 12541–12552 (2019).
Haarnoja, T., Hartikainen, K., Abbeel, P. & Levine, S. Latent space policies for hierarchical reinforcement learning. In Proc. International Conference on Machine Learning ( ICML ) (eds. Dy, J. & Krause, A.) Vol. 4, 2965–2975 (PMLR, 2018).
Rasmussen, D., Voelker, A. & Eliasmith, C. A neural model of hierarchical reinforcement learning. PLoS ONE 12 , e0180234 (2017).
Riedmiller, M. et al. Learning by playing—solving sparse reward tasks from scratch. In Proc. International Conference on Machine Learning ( ICML ) (eds. Dy, J. & Krause, A.) Vol. 10, 6910–6919 (PMLR, 2018).
Yang, F., Lyu, D., Liu, B. & Gustafson, S. PEORL: integrating symbolic planning and hierarchical reinforcement learning for robust decision-making. In Proc. 27th International Joint Conference on Artificial Intelligence ( IJCAI ) (ed. Lang, J.) 4860–4866 (IJCAI, 2018).
Machado, M. C., Bellemare, M. G. & Bowling, M. A Laplacian framework for option discovery in reinforcement learning. In Proc. International Conference on Machine Learning (ICML) (eds. Precup, D. & Teh, Y. W.) Vol. 5, 3567–3582 (PMLR, 2017).
Pathak, D., Agrawal, P., Efros, A. A. & Darrell, T. Curiosity-driven exploration by self-supervised prediction. In Proc. 34th International Conference on Machine Learning ( ICML ) (eds. Precup, D. & Teh, Y. W.) 2778–2787 (PMLR, 2017).
Schillaci, G. et al. Intrinsic motivation and episodic memories for robot exploration of high-dimensional sensory spaces. Adaptive Behav. 29 549–566 (2020).
Colas, C., Fournier, P., Sigaud, O., Chetouani, M. & Oudeyer, P.-Y. CURIOUS: intrinsically motivated modular multi-goal reinforcement learning. In Proc. International Conference on Machine Learning ( ICML ) (eds. Chaudhuri, K. & Salakhutdinov, R.) 1331–1340 (PMLR, 2019).
Hafez, M. B., Weber, C., Kerzel, M. & Wermter, S. Improving robot dual-system motor learning with intrinsically motivated meta-control and latent-space experience imagination. Robot. Auton. Syst. 133 , 103630 (2020).
Yamamoto, K., Onishi, T. & Tsuruoka, Y. Hierarchical reinforcement learning with abductive planning. In Proc. ICML / IJCAI / AAMAS 2018 Workshop on Planning and Learning ( PAL-18 ) (2018).
Wu, B., Gupta, J. K. & Kochenderfer, M. J. Model primitive hierarchical lifelong reinforcement learning . In Proc. International Joint Conference on Autonomous Agents and Multiagent Systems ( AAMAS ) (eds. Agmon, N. et al.) Vol. 1, 34–42 (IFAAMAS, 2019).
Li, Z., Narayan, A. & Leong, T. Y. An efficient approach to model-based hierarchical reinforcement learning. In Proc. 31st AAAI Conference on Artificial Intelligence 3583–3589 (AAAI, 2017).
Hafner, D., Lillicrap, T. & Norouzi, M. Dream to control: learning behaviors by latent imagination. In Proc. International Conference on Learning Representations https://openreview.net/pdf?id=S1lOTC4tDS (ICLR, 2020).
Deisenroth, M. P., Rasmussen, C. E. & Fox, D. Learning to control a low-cost manipulator using data-efficient reinforcement learning. In Robotics : Science and Systems VII ( RSS ) (eds. Durrant-Whyte, H. et al.) 57–64 (Robotics: Science and Systems Foundation, 2011).
Ha, D. & Schmidhuber, J. Recurrent world models facilitate policy evolution. In Proc. 32nd International Conference on Neural Information Processing Systems (NeurIPS) (eds. Bengio, S. et al.) 2455–2467 (NIPS, 2018).
Battaglia, P. W. et al. Relational inductive biases, deep learning and graph networks. Preprint at https://arxiv.org/abs/1806.01261 (2018).
Andrychowicz, M. et al. Hindsight experience replay. In Proc. Neural Information Processing Systems ( NIPS ) (eds. Guyon I. et al.) 5048–5058 (NIPS, 2017); https://papers.nips.cc/paper/7090-hindsight-experience-replay.pdf
Schwartenbeck, P. et al. Computational mechanisms of curiosity and goal-directed exploration. eLife 8 , e41703 (2019).
Haarnoja, T., Zhou, A., Abbeel, P. & Levine, S. Soft actor-critic: off-policy maximum entropy deep reinforcement learning with a stochastic actor. In Proc. International Conference on Machine Learning ( ICML ) (eds. Dy, J. & Krause, A.) 1861–1870 (PMLR, 2018).
Yu, A. J. & Dayan, P. Uncertainty, neuromodulation and attention. Neuron 46 , 681–692 (2005).
Baldwin, D. A. & Kosie, J. E. How does the mind render streaming experience as events? Top. Cogn. Sci. 13 , 79–105 (2021).
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We acknowledge funding from the DFG (projects IDEAS, LeCAREbot, TRR169, SPP 2134, RTG 1808 and EXC 2064/1), the Humboldt Foundation and Max Planck Research School IMPRS-IS.
Manfred Eppe
Present address: Hamburg University of Technology, Hamburg, Germany
Universität Hamburg, Hamburg, Germany
Manfred Eppe, Matthias Kerzel, Phuong D. H. Nguyen & Stefan Wermter
University of Tübingen, Tübingen, Germany
Christian Gumbsch & Martin V. Butz
Max Planck Institute for Intelligent Systems, Tübingen, Germany
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How leaders are using ai as a problem-solving tool.
Leaders face more complex decisions than ever before. For example, many must deliver new and better services for their communities while meeting sustainability and equity goals. At the same time, many need to find ways to operate and manage their budgets more efficiently. So how can these leaders make complex decisions and get them right in an increasingly tricky business landscape? The answer lies in harnessing technological tools like Artificial Intelligence (AI).
CHONGQING, CHINA - AUGUST 22: A visitor interacts with a NewGo AI robot during the Smart China Expo ... [+] 2022 on August 22, 2022 in Chongqing, China. The expo, held annually in Chongqing since 2018, is a platform to promote global exchanges of smart technologies and international cooperation in the smart industry. (Photo by Chen Chao/China News Service via Getty Images)
What is AI?
AI can help leaders in several different ways. It can be used to process and make decisions on large amounts of data more quickly and accurately. AI can also help identify patterns and trends that would otherwise be undetectable. This information can then be used to inform strategic decision-making, which is why AI is becoming an increasingly important tool for businesses and governments. A recent study by PwC found that 52% of companies accelerated their AI adoption plans in the last year. In addition, 86% of companies believe that AI will become a mainstream technology at their company imminently. As AI becomes more central in the business world, leaders need to understand how this technology works and how they can best integrate it into their operations.
At its simplest, AI is a computer system that can learn and work independently without human intervention. This ability makes AI a powerful tool. With AI, businesses and public agencies can automate tasks, get insights from data, and make decisions with little or no human input. Consequently, AI can be a valuable problem-solving tool for leaders across the private and public sectors, primarily through three methods.
1) Automation
One of AI’s most beneficial ways to help leaders is by automating tasks. This can free up time to focus on other essential things. For example, AI can help a city save valuable human resources by automating parking enforcement. In addition, this will help improve the accuracy of detecting violations and prevent costly mistakes. Automation can also help with things like appointment scheduling and fraud detection.
2) Insights from data
Another way AI can help leaders solve problems is by providing insights from data. With AI, businesses can gather large amounts of data and then use that data to make better decisions. For example, suppose a company is trying to decide which products to sell. In that case, AI can be used to gather data about customer buying habits and then use that data to make recommendations about which products to market.
Best covid-19 travel insurance plans.
3) Simulations
Finally, AI can help leaders solve problems by allowing them to create simulations. With AI, organizations can test out different decision scenarios and see what the potential outcomes could be. This can help leaders make better decisions by examining the consequences of their choices. For example, a city might use AI to simulate different traffic patterns to see how a new road layout would impact congestion.
Choosing the Right Tools
Artificial intelligence and machine learning technologies can revolutionize how governments and businesses solve real-world problems,” said Chris Carson, CEO of Hayden AI, a global leader in intelligent enforcement technologies powered by artificial intelligence. His company addresses a problem once thought unsolvable in the transit world: managing illegal parking in bus lanes in a cost effective, scalable way.
Illegal parking in bus lanes is a major problem for cities and their transit agencies. Cars and trucks illegally parked in bus lanes force buses to merge into general traffic lanes, significantly slowing down transit service and making riders’ trips longer. That’s where a company like Hayden AI comes in. “Hayden AI uses artificial intelligence and machine learning algorithms to detect and process illegal parking in bus lanes in real-time so that cities can take proactive measures to address the problem ,” Carson observes.
Illegal parking in bus lanes is a huge problem for transit agencies. Hayden AI works with transit ... [+] agencies to fix this problem by installing its AI-powered camera systems on buses to conduct automated enforcement of parking violations in bus lanes
In this case, an AI-powered camera system is installed on each bus. The camera system uses computer vision to “watch” the street for illegal parking in the bus lane. When it detects a traffic violation, it sends the data back to the parking authority. This allows the parking authority to take action, such as sending a ticket to the offending vehicle’s owner.
The effectiveness of AI is entirely dependent on how you use it. As former Accenture chief technology strategist Bob Suh notes in the Harvard Business Review, problem-solving is best when combined with AI and human ingenuity. “In other words, it’s not about the technology itself; it’s about how you use the technology that matters. AI is not a panacea for all ills. Still, when incorporated into a company’s problem-solving repertoire, it can be an enormously powerful tool,” concludes Terence Mauri, founder of Hack Future Lab, a global think tank.
Split the Responsibility
Huda Khan, an academic researcher from the University of Aberdeen, believes that AI is critical for international companies’ success, especially in the era of disruption. Khan is calling international marketing academics’ research attention towards exploring such transformative approaches in terms of how these inform competitive business practices, as are international marketing academics Michael Christofi from the Cyprus University of Technology; Richard Lee from the University of South Australia; Viswanathan Kumar from St. John University; and Kelly Hewett from the University of Tennessee. “AI is very good at automating repetitive tasks, such as customer service or data entry. But it’s not so good at creative tasks, such as developing new products,” Khan says. “So, businesses need to think about what tasks they want to automate and what tasks they want to keep for humans.”
Khan believes that businesses need to split the responsibility between AI and humans. For example, Hayden AI’s system is highly accurate and only sends evidence packages of potential violations for human review. Once the data is sent, human analysis is still needed to make the final decision. But with much less work to do, government agencies can devote their employees to tasks that can’t be automated.
Backed up by efficient, effective data analysis, human problem-solving can be more innovative than ever. Like all business transitions, developing the best system for combining human and AI work might take some experimentation, but it can significantly impact future success. For example, if a company is trying to improve its customer service, it can use AI startup Satisfi’s natural language processing technology . This technology can understand a customer’s question and find the best answer from a company’s knowledge base. Likewise, if a company tries to increase sales, it can use AI startup Persado’s marketing language generation technology . This technology can be used to create more effective marketing campaigns by understanding what motivates customers and then generating language that is more likely to persuade them to make a purchase.
Look at the Big Picture
A technological solution can frequently improve performance in multiple areas simultaneously. For instance, Hayden AI’s automated enforcement system doesn’t just help speed up transit by keeping bus lanes clear for buses; it also increases data security by limiting how much data is kept for parking enforcement, which allows a city to increase the efficiency of its transportation while also protecting civil liberties.
This is the case with many technological solutions. For example, an e-commerce business might adopt a better data architecture to power a personalized recommendation option and benefit from improved SEO. As a leader, you can use your big-picture view of your company to identify critical secondary benefits of technologies. Once you have the technologies in use, you can also fine-tune your system to target your most important priorities at once.
In summary, AI technology is constantly evolving, becoming more accessible and affordable for businesses of all sizes. By harnessing the power of AI, leaders can make better decisions, improve efficiency, and drive innovation. However, it’s important to remember that AI is not a silver bullet. Therefore, organizations must use AI and humans to get the best results.
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Problem Solving in Artificial Intelligence
The abstraction is useful if carrying out each of the actions in the solution is easier than the original problem. 3.2 Example Problems A standardized problem is intended to illustrate or exercise various problem-solving methods. It can be given a concise, exact description and hence is suitable as a benchmark for researchers to compare the ...
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Figure 1: A simplified road map of part of Romania. The problem is to travel from Arad to Bucharest in a day. For the agent, the goal will be to reach Bucharest the following day.
6.825 Techniques in Artificial Intelligence Problem Solving and Search Problem Solving • Agent knows world dynamics • World state is finite, small enough to enumerate • World is deterministic • Utility for a sequence of states is a sum over path The utility for sequences of states is a sum over the path of the utilities of the
There are many problems for which we know specialized ways of solving them. For example: Problem: "find the roots of the polynomial ax + bx2 + c = 0". We know mechanical procedures to solve this problem in an exact way. However, those procedures can only be applied to this problem, but not to any other.
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