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... • Almost all expert systems also have an explanation subsystem, which allows the program to explain its reasoning to the user • Some systems also have a knowledge base editor which help the expert or knowledge engineer to easily update and check the knowledge base ...
... • Almost all expert systems also have an explanation subsystem, which allows the program to explain its reasoning to the user • Some systems also have a knowledge base editor which help the expert or knowledge engineer to easily update and check the knowledge base ...
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... and incomplete depending on whether they can find the optimal assignment. Complete algorithms can determine optimal assignments but they are computationally expensive and are not appropriate in ...
... and incomplete depending on whether they can find the optimal assignment. Complete algorithms can determine optimal assignments but they are computationally expensive and are not appropriate in ...
TTTPLOTS: A PERL PROGRAM TO CREATE TIME-TO
... in the comparison of different algorithms or strategies for solving a given problem and have been widely used as a tool for algorithm design and comparison. In the next section, we discuss how TTT plots are generated, following closely Aiex, Resende, and Ribeiro [4]. The perl program tttplots.pl is ...
... in the comparison of different algorithms or strategies for solving a given problem and have been widely used as a tool for algorithm design and comparison. In the next section, we discuss how TTT plots are generated, following closely Aiex, Resende, and Ribeiro [4]. The perl program tttplots.pl is ...
Lecture 20 Reinforcement Learning I
... We consider the problem of learning to follow a desired trajectory when given a small number of demonstrations from a sub-optimal expert. We present an algorithm that (i) extracts the desired trajectory from the sub-optimal expert's demonstrations and (ii) learns a local model suitable for control a ...
... We consider the problem of learning to follow a desired trajectory when given a small number of demonstrations from a sub-optimal expert. We present an algorithm that (i) extracts the desired trajectory from the sub-optimal expert's demonstrations and (ii) learns a local model suitable for control a ...
CAPTCHA: Using Hard AI Problems For Security
... pass, but current computer programs can’t pass: any program that has high success over a captcha can be used to solve an unsolved Artificial Intelligence (AI) problem. We provide several novel constructions of captchas. Since captchas have many applications in practical security, our approach introd ...
... pass, but current computer programs can’t pass: any program that has high success over a captcha can be used to solve an unsolved Artificial Intelligence (AI) problem. We provide several novel constructions of captchas. Since captchas have many applications in practical security, our approach introd ...
Inverse Reinforcement Learning in Relational Domains
... In this work, we introduce the first approach to the Inverse Reinforcement Learning (IRL) problem in relational domains. IRL has been used to recover a more compact representation of the expert policy leading to better generalization performances among different contexts. On the other hand, relation ...
... In this work, we introduce the first approach to the Inverse Reinforcement Learning (IRL) problem in relational domains. IRL has been used to recover a more compact representation of the expert policy leading to better generalization performances among different contexts. On the other hand, relation ...
Distribution of Strategies in a Spatial Multi-Agent Game
... earning less than they would earn by collaborating. Thus, the number of games ng two agents play together becomes important. For ng ≥ 2, this is called an iterated Prisoner’s Dilemma (IPD). It makes sense only if the agents can remember the previous choices of their opponents, i.e. if they have a me ...
... earning less than they would earn by collaborating. Thus, the number of games ng two agents play together becomes important. For ng ≥ 2, this is called an iterated Prisoner’s Dilemma (IPD). It makes sense only if the agents can remember the previous choices of their opponents, i.e. if they have a me ...
Jurek Gryz The frame problem in artificial intelligence and
... remind ourselves that the distance of A from the Sun has not changed, neither did its temperature or color or any other properties. Last, but not least, such axioms are context-dependent. In a scenario where removing B from A causes D to be put on C, the axiom specified above is false. When an actio ...
... remind ourselves that the distance of A from the Sun has not changed, neither did its temperature or color or any other properties. Last, but not least, such axioms are context-dependent. In a scenario where removing B from A causes D to be put on C, the axiom specified above is false. When an actio ...
Classification Problem Solving
... classes are stereotypes that are hierarchically organized, and the process of identification is one of matching observations of an unknown entity against features of known classes. A paradigmatic example is identification of a plant or animal, using a guidebook of features, such as coloration, struc ...
... classes are stereotypes that are hierarchically organized, and the process of identification is one of matching observations of an unknown entity against features of known classes. A paradigmatic example is identification of a plant or animal, using a guidebook of features, such as coloration, struc ...
Simulated Annealing - School of Computer Science
... If possible, the cost function should also be designed so that it can lead the search. One way of achieving this is to avoid cost functions where many states return the same value. This can be seen as representing a plateau in the search space which the search has no knowledge about which way it sho ...
... If possible, the cost function should also be designed so that it can lead the search. One way of achieving this is to avoid cost functions where many states return the same value. This can be seen as representing a plateau in the search space which the search has no knowledge about which way it sho ...
A Similarity Evaluation Technique for Cooperative Problem
... some cases it seems to be possible to accept selection proposals from the agent Fox if they concern Excite and Yahoo. All four agents are expected to give an acceptable selection concerning “Artificial Intelligence” related search and only suggestion of the agent Cat can be accepted if it concerns “ ...
... some cases it seems to be possible to accept selection proposals from the agent Fox if they concern Excite and Yahoo. All four agents are expected to give an acceptable selection concerning “Artificial Intelligence” related search and only suggestion of the agent Cat can be accepted if it concerns “ ...
using simulation and neural networks to develop a scheduling advisor
... indicate the position of the job in the queue that must be processed the neural network should be trained using a data set of decisions with the associated attribute variables. The data set should have the form of two matrices the first should include the decisions and the second the value of each a ...
... indicate the position of the job in the queue that must be processed the neural network should be trained using a data set of decisions with the associated attribute variables. The data set should have the form of two matrices the first should include the decisions and the second the value of each a ...
Rollout Sampling Policy Iteration for Decentralized POMDPs
... A new policy representation allows us to represent solutions compactly. The key benefits of the algorithm are its linear time complexity over the number of agents, its bounded memory usage and good solution quality. It can solve larger problems that are intractable for existing planning algorithms. ...
... A new policy representation allows us to represent solutions compactly. The key benefits of the algorithm are its linear time complexity over the number of agents, its bounded memory usage and good solution quality. It can solve larger problems that are intractable for existing planning algorithms. ...
Comparative Performance of Simulated Annealing and
... both the methods. We wanted to create random instances of the NSP that were realistic and indicative of real life situations. For this purpose, we collected data about nurse rosters from well-known Peerless Hospital in Kolkata. Duration of roster varies in different hospitals, so we decided to keep ...
... both the methods. We wanted to create random instances of the NSP that were realistic and indicative of real life situations. For this purpose, we collected data about nurse rosters from well-known Peerless Hospital in Kolkata. Duration of roster varies in different hospitals, so we decided to keep ...
PPT - ConSystLab - University of Nebraska–Lincoln
... We extend the empirical study of a multi-agent search method for solving a Constraint Satisfaction Problem (CSP). We compare this method's performance with that of a local search (LS) and a systematic (BT) search, in the context of a real-world application that is over-constrainedthe assignment of ...
... We extend the empirical study of a multi-agent search method for solving a Constraint Satisfaction Problem (CSP). We compare this method's performance with that of a local search (LS) and a systematic (BT) search, in the context of a real-world application that is over-constrainedthe assignment of ...
Formula-Based Probabilistic Inference - Washington
... practice. These and other ideas form the backbone of many state-of-the-art schemes such as ACE (Chavira and Darwiche, 2008) and Cachet (Sang et al., 2005). To simplify a PropMRF, we can apply any parsimonious operators - operators which do not change its partition function. In particular, we can rem ...
... practice. These and other ideas form the backbone of many state-of-the-art schemes such as ACE (Chavira and Darwiche, 2008) and Cachet (Sang et al., 2005). To simplify a PropMRF, we can apply any parsimonious operators - operators which do not change its partition function. In particular, we can rem ...
Multi-agent MDP and POMDP Models for Coordination
... initialize random policies for all agents for each agent i: fix the policies for all other agents find the optimal policy for agent i ...
... initialize random policies for all agents for each agent i: fix the policies for all other agents find the optimal policy for agent i ...
Resources - CSE, IIT Bombay
... Nodes from open list are taken in some order, expanded and children are put into open list and parent is put into closed list. Assumption: Monotone restriction is satisfied. That is the estimated cost of reaching the goal node for a particular node is no more than the cost of reaching a child and th ...
... Nodes from open list are taken in some order, expanded and children are put into open list and parent is put into closed list. Assumption: Monotone restriction is satisfied. That is the estimated cost of reaching the goal node for a particular node is no more than the cost of reaching a child and th ...
Igor Kiselev - University of Waterloo
... more simple and more complex algorithms (e.g. AWESOME [Conitzer and Sandholm 2003]) Classification of situations (games) with various values of the delta and alpha variables: what values are good in what situations. Extending work to have more players. Online learning and exploration policy in stoch ...
... more simple and more complex algorithms (e.g. AWESOME [Conitzer and Sandholm 2003]) Classification of situations (games) with various values of the delta and alpha variables: what values are good in what situations. Extending work to have more players. Online learning and exploration policy in stoch ...
Memory-Bounded Dynamic Programming for DEC
... under uncertainty. The Markov decision process (MDP) has been proved to be a useful framework for centralized decision making in fully observable stochastic environments. In the 1960’s, partially observable MDPs (POMDPs) were introduced to account for imperfect state information. An even more genera ...
... under uncertainty. The Markov decision process (MDP) has been proved to be a useful framework for centralized decision making in fully observable stochastic environments. In the 1960’s, partially observable MDPs (POMDPs) were introduced to account for imperfect state information. An even more genera ...
Optimal 2-constraint satisfaction via sum
... While the improvements over Williams’s results concern the polynomial factor only, for potential practical implementations the improvement is important. If, for example, the number of constraint m grows as Θ(n2 ), then the improvement is roughly by the factor n4 . In our analysis we do not assume th ...
... While the improvements over Williams’s results concern the polynomial factor only, for potential practical implementations the improvement is important. If, for example, the number of constraint m grows as Θ(n2 ), then the improvement is roughly by the factor n4 . In our analysis we do not assume th ...
Artificial Intelligence
... • E.g., performance measure of a vacuum‐cleaner agent could be amount of dirt cleaned up, amount of time taken, amount of electricity consumed, amount of noise generated, etc. ...
... • E.g., performance measure of a vacuum‐cleaner agent could be amount of dirt cleaned up, amount of time taken, amount of electricity consumed, amount of noise generated, etc. ...
Kære kollegaer,
... build the best tree? Using the so-called ID3 algorithm, one of the most effective algorithms for induction, may solve this problem. The algorithm builds a tree while striving at as simple a tree as possible. The assumption is that a simple tree performs better than a complex tree when unknown data a ...
... build the best tree? Using the so-called ID3 algorithm, one of the most effective algorithms for induction, may solve this problem. The algorithm builds a tree while striving at as simple a tree as possible. The assumption is that a simple tree performs better than a complex tree when unknown data a ...
Zhang Yufeng - USD Biology
... • The number of total trials was largest during the second 10 min period in the water trials in the SDR condition, although there was no significant difference in the food trials. • Decrease over time in the number of total trials shown by the significant main effect of Time in both food and water ...
... • The number of total trials was largest during the second 10 min period in the water trials in the SDR condition, although there was no significant difference in the food trials. • Decrease over time in the number of total trials shown by the significant main effect of Time in both food and water ...
Resources - CSE, IIT Bombay
... 2. Create a list called CLOSED that is initially empty. 3. Loop: if OPEN is empty, exit with failure. 4. Select the first node on OPEN, remove from OPEN and put on CLOSED, call this node n. 5. if n is the goal node, exit with the solution obtained by tracing a path along the pointers from n to s in ...
... 2. Create a list called CLOSED that is initially empty. 3. Loop: if OPEN is empty, exit with failure. 4. Select the first node on OPEN, remove from OPEN and put on CLOSED, call this node n. 5. if n is the goal node, exit with the solution obtained by tracing a path along the pointers from n to s in ...
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.