On the Structural Robustness of Evolutionary Models of Cooperation
... learn), or the population of units as a whole may adapt through an evolutionary process (or both). While formalizing the problem of cooperation in this way significantly decreases its complexity (and generality), the question still remains largely unspecified: how many units form the population? How ...
... learn), or the population of units as a whole may adapt through an evolutionary process (or both). While formalizing the problem of cooperation in this way significantly decreases its complexity (and generality), the question still remains largely unspecified: how many units form the population? How ...
Eagleman Ch 14. Motivation and Reward
... Rewards increase the motivation to engage in a particular behavior. Punishments make it less likely to engage in that particular behavior. Primary rewards directly affect homeostasis. Secondary rewards are associated with primary rewards. ...
... Rewards increase the motivation to engage in a particular behavior. Punishments make it less likely to engage in that particular behavior. Primary rewards directly affect homeostasis. Secondary rewards are associated with primary rewards. ...
Intelligent Agents
... state of the environment at each point in time. • Deterministic (vs. stochastic): The next state of the environment is completely determined by the current state and the action executed by the agent. ...
... state of the environment at each point in time. • Deterministic (vs. stochastic): The next state of the environment is completely determined by the current state and the action executed by the agent. ...
Solving the Round Robin Problem Using Propositional Logic
... & Manyà 1999c). We found solutions for 16 teams in about 2 hours. In this paper we show experimentally that, by combining local search satisfiability algorithms and an appropriate problem encoding based on classical propositional logic, we can find feasible schedules many times faster than using th ...
... & Manyà 1999c). We found solutions for 16 teams in about 2 hours. In this paper we show experimentally that, by combining local search satisfiability algorithms and an appropriate problem encoding based on classical propositional logic, we can find feasible schedules many times faster than using th ...
The Economic Optimization of Mining Support Scheme Based on
... cases, the goal of any support system is to achieve safety production and economical cost. The support needed to accomplish this objective depends on the mechanical computing and economical optimization. From the mechanical view, many researchers have done many works range from the rock properties, ...
... cases, the goal of any support system is to achieve safety production and economical cost. The support needed to accomplish this objective depends on the mechanical computing and economical optimization. From the mechanical view, many researchers have done many works range from the rock properties, ...
Towards Adversarial Reasoning in Statistical Relational Domains
... spammers in order to understand their behavior, develop better defenses, and evaluate the robustness of these defenses. The spammer’s opponent is the search engine’s anti-spam component, which uses an MLN to label web pages as spam or non-spam, after they have been modified by the spammer. We view t ...
... spammers in order to understand their behavior, develop better defenses, and evaluate the robustness of these defenses. The spammer’s opponent is the search engine’s anti-spam component, which uses an MLN to label web pages as spam or non-spam, after they have been modified by the spammer. We view t ...
finalReport - Suraj @ LUMS
... Throughout the course of the lecture the students will continually be reminded of big picture and how AI is related to their lives by means of examples (for instance the applications of AI in games such as Counter Strike or Chess, and other software and hardware products) connecting what they are le ...
... Throughout the course of the lecture the students will continually be reminded of big picture and how AI is related to their lives by means of examples (for instance the applications of AI in games such as Counter Strike or Chess, and other software and hardware products) connecting what they are le ...
Lecture VII--InferenceInBayesianNet
... Convergence can be very slow with probabilities close to 1 or 0 Can handle arbitrary combinations of discrete and continuous variables ...
... Convergence can be very slow with probabilities close to 1 or 0 Can handle arbitrary combinations of discrete and continuous variables ...
Essential Thinking. Introduction to Problem Solving Example
... Polynomial –> 0 Does there exists a polynomial function satisfying the following conditions: it is always strictly greater than zero, for and (small) > 0, there exists a value of the polinomial less then Proove or Disproove. ...
... Polynomial –> 0 Does there exists a polynomial function satisfying the following conditions: it is always strictly greater than zero, for and (small) > 0, there exists a value of the polinomial less then Proove or Disproove. ...
Report - Suraj @ LUMS
... Throughout the course of the lecture the students will continually be reminded of big picture and how AI is related to their lives by means of examples (for instance the applications of AI in games such as Counter Strike or Chess, and other software and hardware products) connecting what they are le ...
... Throughout the course of the lecture the students will continually be reminded of big picture and how AI is related to their lives by means of examples (for instance the applications of AI in games such as Counter Strike or Chess, and other software and hardware products) connecting what they are le ...
as a PDF
... published only recently; future papers describing its operation and performance will allow a more detailed comparison with BECCA’s feature creator. ...
... published only recently; future papers describing its operation and performance will allow a more detailed comparison with BECCA’s feature creator. ...
1983 - Derivational Analogy and Its Role in Problem Solving
... concerned with the other two approaches, as the they could conceivably provide powerful reasoning mechanisms not heretofore analyzed in the context of automatirg problem-solving processes. The rest of this paper focuses on a new formulation of the analogical problem solving approach. ...
... concerned with the other two approaches, as the they could conceivably provide powerful reasoning mechanisms not heretofore analyzed in the context of automatirg problem-solving processes. The rest of this paper focuses on a new formulation of the analogical problem solving approach. ...
Object Focused Q-learning for Autonomous Agents
... along with when to apply each one, i.e., where to focus at each moment. Our algorithm is appropriate for many real-world scenarios, offers exponential speed-ups over traditional RL on domains composed of independent objects and is not constrained to a fixed-length feature vector. The setup of our al ...
... along with when to apply each one, i.e., where to focus at each moment. Our algorithm is appropriate for many real-world scenarios, offers exponential speed-ups over traditional RL on domains composed of independent objects and is not constrained to a fixed-length feature vector. The setup of our al ...
An Exhaustive Survey on Nature Inspired Optimization
... Once a ant find food, it returns to colony and leave a trail of chemical substances called pheromone along the path. Other ants of the swarm can sense pheromone trails and move on the same path. The interesting point is that how often the path visit by ants is determined by the concentration of pher ...
... Once a ant find food, it returns to colony and leave a trail of chemical substances called pheromone along the path. Other ants of the swarm can sense pheromone trails and move on the same path. The interesting point is that how often the path visit by ants is determined by the concentration of pher ...
An Application of Ant Colony Optimization to Image Clustering
... Content-based image retrieval can be dramatically improved by providing a good initial clustering of visual data. The problem of image clustering is that most current algorithms are not able to identify individual clusters that exist in different feature subspaces. In this paper, we propose a novel ...
... Content-based image retrieval can be dramatically improved by providing a good initial clustering of visual data. The problem of image clustering is that most current algorithms are not able to identify individual clusters that exist in different feature subspaces. In this paper, we propose a novel ...
Why Probability?
... Unified approach to reasoning, parameter learning, structure learning Principled combination of KE with learning Can learn from small, moderate and large samples Many general-purpose exact and approximate algorithms with strong theoretical justification and practical success – Good results (better t ...
... Unified approach to reasoning, parameter learning, structure learning Principled combination of KE with learning Can learn from small, moderate and large samples Many general-purpose exact and approximate algorithms with strong theoretical justification and practical success – Good results (better t ...
Exploiting Anonymity and Homogeneity in Factored
... Expected Agent or EA objective. Due to law of large numbers, EA objective was shown to provide good performance on problems with large number of agents. However, Varakantham et al.’s approach has two aspects that can be improved: (1) Reward functions are approximated using piecewise constant or piec ...
... Expected Agent or EA objective. Due to law of large numbers, EA objective was shown to provide good performance on problems with large number of agents. However, Varakantham et al.’s approach has two aspects that can be improved: (1) Reward functions are approximated using piecewise constant or piec ...
On the Non-Existence of a Universal Learning Algorithm for
... One should stress once again that the fact that no general algorithm exists for higher order or recurrent networks, which could solve the loading problem (for all its instances), does not imply that all instances of this problem are unsolvable or that no solutions exist. One could hope, that in most ...
... One should stress once again that the fact that no general algorithm exists for higher order or recurrent networks, which could solve the loading problem (for all its instances), does not imply that all instances of this problem are unsolvable or that no solutions exist. One could hope, that in most ...
CoursePortfolioCS435
... behavior of the human to allow computers to perceive, reason, and decide. ...
... behavior of the human to allow computers to perceive, reason, and decide. ...
An Alternative Arithmetic Approach to the Water Jugs Problem
... Programming and Psychology. The solution of the problem mainly based on heuristic approach or some search methods such as Breadth First Search (BFS) or Depth First Search (DFS) or Diophantine approach. In BFS, we will be certainly reaching the goal but time taken to reach the goal will be too much. ...
... Programming and Psychology. The solution of the problem mainly based on heuristic approach or some search methods such as Breadth First Search (BFS) or Depth First Search (DFS) or Diophantine approach. In BFS, we will be certainly reaching the goal but time taken to reach the goal will be too much. ...
Paper
... of the model and basic results such as the existence of optimal solutions and algorithms for :finding them. These algorithms still form the basis of nearly all the current approaches to solving MDPs. In MDPs, an agent has the task of making decisions to solve some planning problem. The agent is embe ...
... of the model and basic results such as the existence of optimal solutions and algorithms for :finding them. These algorithms still form the basis of nearly all the current approaches to solving MDPs. In MDPs, an agent has the task of making decisions to solve some planning problem. The agent is embe ...
This project is your chance to show your creativity, in particular, by
... mean µ1 and unknown variance σ 2 , and let Y1 , . . . , Yn be an independent random sample from a normal population with unknown mean µ2 and variance kσ 2 , where k > 0 is known. Construct a 100(1 − α)% confidence interval for µ1 − µ2 . [You might be able to find a more detailed outline of the solut ...
... mean µ1 and unknown variance σ 2 , and let Y1 , . . . , Yn be an independent random sample from a normal population with unknown mean µ2 and variance kσ 2 , where k > 0 is known. Construct a 100(1 − α)% confidence interval for µ1 − µ2 . [You might be able to find a more detailed outline of the solut ...
Case-Based Reasoning Special Track on
... Case-based reasoning (CBR) is an artificial intelligence problem solving and analysis methodology that retrieves and adapts previous experiences to fit new contexts. In CBR systems, expertise is embodied in a library of past cases, rather than being encoded in classical rules. A new problem is solve ...
... Case-based reasoning (CBR) is an artificial intelligence problem solving and analysis methodology that retrieves and adapts previous experiences to fit new contexts. In CBR systems, expertise is embodied in a library of past cases, rather than being encoded in classical rules. A new problem is solve ...
Multi-armed bandit
In probability theory, the multi-armed bandit problem (sometimes called the K- or N-armed bandit problem) is a problem in which a gambler at a row of slot machines (sometimes known as ""one-armed bandits"") has to decide which machines to play, how many times to play each machine and in which order to play them. When played, each machine provides a random reward from a distribution specific to that machine. The objective of the gambler is to maximize the sum of rewards earned through a sequence of lever pulls.Robbins in 1952, realizing the importance of the problem, constructed convergent population selection strategies in ""some aspects of the sequential design of experiments"".A theorem, the Gittins index published first by John C. Gittins gives an optimal policy in the Markov setting for maximizing the expected discounted reward.In practice, multi-armed bandits have been used to model the problem of managing research projects in a large organization, like a science foundation or a pharmaceutical company. Given a fixed budget, the problem is to allocate resources among the competing projects, whose properties are only partially known at the time of allocation, but which may become better understood as time passes.In early versions of the multi-armed bandit problem, the gambler has no initial knowledge about the machines. The crucial tradeoff the gambler faces at each trial is between ""exploitation"" of the machine that has the highest expected payoff and ""exploration"" to get more information about the expected payoffs of the other machines. The trade-off between exploration and exploitation is also faced in reinforcement learning.