Strategies and Design for Interleaving Reasoning and Selection of Axioms

... Reasoning based on starting point utilizes the user background and provides most important reasoning results to users. These strategies is aimed at satisfying a wide variety of user needs and removing the scalability barriers. Anytime algorithms are attractive for Web scale reasoning, because they a ...

... Reasoning based on starting point utilizes the user background and provides most important reasoning results to users. These strategies is aimed at satisfying a wide variety of user needs and removing the scalability barriers. Anytime algorithms are attractive for Web scale reasoning, because they a ...

Structure and Inference In Classical Planning Nir Lipovetzky TESI DOCTORAL UPF / 2012

... helpful insights, Anders Jonsson and Victor Dalmau. It has been a privilege to share the doctoral studies with people like Ayman Moghnieh, Rida Sadek and Pablo Arias, and many Bachelor students of UPF. I want to mention also the special support given by the DTIC administration workers, who dealt wit ...

... helpful insights, Anders Jonsson and Victor Dalmau. It has been a privilege to share the doctoral studies with people like Ayman Moghnieh, Rida Sadek and Pablo Arias, and many Bachelor students of UPF. I want to mention also the special support given by the DTIC administration workers, who dealt wit ...

Structure and Inference in Classical Planning

... many of the states in the problem. Their performance has been improved by devising new search algorithms, and new heuristic estimators. On the other hand, simple problems that should be solved with almost no search, still require the planners to perform extensive search before finding a solution. Co ...

... many of the states in the problem. Their performance has been improved by devising new search algorithms, and new heuristic estimators. On the other hand, simple problems that should be solved with almost no search, still require the planners to perform extensive search before finding a solution. Co ...

neuronal reward and decision signals: from theories to data

... mediated by neuronal reward prediction error signals which implement basic constructs of reinforcement learning theory. These signals are found in dopamine neurons, which emit a global reward signal to striatum and frontal cortex, and in specific neurons in striatum, amygdala, and frontal cortex pro ...

... mediated by neuronal reward prediction error signals which implement basic constructs of reinforcement learning theory. These signals are found in dopamine neurons, which emit a global reward signal to striatum and frontal cortex, and in specific neurons in striatum, amygdala, and frontal cortex pro ...

pdf file - The Department of Computer Science

... not decidable as solutions have to be integers and the relaxation does not relax this property. Discretization. In order to restrict the number of possible values from the enumeration approach, multiple values can be aggregated into “buckets”, where a representative approximates all values within. T ...

... not decidable as solutions have to be integers and the relaxation does not relax this property. Discretization. In order to restrict the number of possible values from the enumeration approach, multiple values can be aggregated into “buckets”, where a representative approximates all values within. T ...

Planning with Markov Decision Processes

... Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its ac ...

... Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its ac ...

Publication : An introduction to Soar as an agent architecture

... Finally, while some symbol systems may attempt to approximate the knowledge level, they will necessarily always fall somewhat short of the perfect rationality of the knowledge level. One can think of the way in which a system falls short of the knowledge level as its particular “psychology;” it may ...

... Finally, while some symbol systems may attempt to approximate the knowledge level, they will necessarily always fall somewhat short of the perfect rationality of the knowledge level. One can think of the way in which a system falls short of the knowledge level as its particular “psychology;” it may ...

artificial intelligence (luger, 6th, 2008)

... Intelligence is too complex to be described by any single theory; instead, researchers are constructing a hierarchy of theories that characterize it at multiple levels of abstraction. At the lowest levels of this hierarchy, neural networks, genetic algorithms and other forms of emergent computation ...

... Intelligence is too complex to be described by any single theory; instead, researchers are constructing a hierarchy of theories that characterize it at multiple levels of abstraction. At the lowest levels of this hierarchy, neural networks, genetic algorithms and other forms of emergent computation ...

Wikibook (pages 1-223)

... phenotype. This indirect encoding is believed to make the genetic search more robust (i.e. reduce the probability of fatal mutations), and also may improve the evolvability of the organism.[1][2] Such indirect (aka generative or developmental) encodings also enable evolution to exploit the regularit ...

... phenotype. This indirect encoding is believed to make the genetic search more robust (i.e. reduce the probability of fatal mutations), and also may improve the evolvability of the organism.[1][2] Such indirect (aka generative or developmental) encodings also enable evolution to exploit the regularit ...

Speeding Up Problem-Solving by Abstraction: A Graph

... solution length and "work". Solution length is of interest because abstraction, in combination with refinement, is not guaranteed to produce optimal solutions. Work is what abstraction aims to reduce: the purpose of abstraction is to speed up problem-solving. We originally measured work in terms of ...

... solution length and "work". Solution length is of interest because abstraction, in combination with refinement, is not guaranteed to produce optimal solutions. Work is what abstraction aims to reduce: the purpose of abstraction is to speed up problem-solving. We originally measured work in terms of ...

... WARNING. On having consulted this thesis you’re accepting the following use conditions: Spreading this thesis by the TDX (www.tesisenxarxa.net) service has been authorized by the titular of the intellectual property rights only for private uses placed in investigation and teaching activities. Reprod ...

Aalborg Universitet

... my second year in computer science and was looking for a summer time job to earn some money. I had contacted IBM and other big companies and it seemed like I would spend most of my vacation in one of these companies. After lunch we were walking back to our group room when my good friend and student ...

... my second year in computer science and was looking for a summer time job to earn some money. I had contacted IBM and other big companies and it seemed like I would spend most of my vacation in one of these companies. After lunch we were walking back to our group room when my good friend and student ...

Page 1 of 14 Retrieval in Case-Based Reasoning: An

... Inspired by previous work by Gentner and Forbus (1991), Börner (1993) proposes an approach to retrieval in which fast retrieval of candidate cases based on their surface similarity to the target problem is followed by a more expensive assessment of their structural similarity. She defines structura ...

... Inspired by previous work by Gentner and Forbus (1991), Börner (1993) proposes an approach to retrieval in which fast retrieval of candidate cases based on their surface similarity to the target problem is followed by a more expensive assessment of their structural similarity. She defines structura ...

Wrappers for feature subset selection

... In supervised machine learning, an induction algorithm is typically presented with a set of training instances, where each instance is described by a vector of feature (or attribute) values and a class label. For example, in medical diagnosis problems the features might include the age, weight, and ...

... In supervised machine learning, an induction algorithm is typically presented with a set of training instances, where each instance is described by a vector of feature (or attribute) values and a class label. For example, in medical diagnosis problems the features might include the age, weight, and ...

State-set branching: Leveraging BDDs for heuristic search

... State-set branching is the first general framework for combining heuristic search and BDD-based search. All previous work has been restricted to particular algorithms. BDD-based heuristic search has been investigated independently in symbolic model checking and AI. The pioneering work is in symbolic ...

... State-set branching is the first general framework for combining heuristic search and BDD-based search. All previous work has been restricted to particular algorithms. BDD-based heuristic search has been investigated independently in symbolic model checking and AI. The pioneering work is in symbolic ...

Soar : an architecture for general intelligence

... artificial intelligence: natural-language parsing, concept learning, and predicate-calculus theorem proving. In each case the performance and knowledge of an existing system has been adopted as a target in order to learn as much as possible by comparison: Dypar [6], Version Spaces [44] and Resolutio ...

... artificial intelligence: natural-language parsing, concept learning, and predicate-calculus theorem proving. In each case the performance and knowledge of an existing system has been adopted as a target in order to learn as much as possible by comparison: Dypar [6], Version Spaces [44] and Resolutio ...

Algorithms for Maximum Satisfiability

... Find truth assignment that satisfies a maximum number of clauses This is the standard definition, much studied in Theoretical Computer Science. ...

... Find truth assignment that satisfies a maximum number of clauses This is the standard definition, much studied in Theoretical Computer Science. ...

PDF

... the solution very close to zero-mean. (The objective changes slightly, but in practice this has minimal impact on the solution.) The fixing of the anchor points allows us to break down the optimization into r independent sub-problems. This can be seen from the fact that by definition all constraints ...

... the solution very close to zero-mean. (The objective changes slightly, but in practice this has minimal impact on the solution.) The fixing of the anchor points allows us to break down the optimization into r independent sub-problems. This can be seen from the fact that by definition all constraints ...

BCA-601

... entering a program easier. Further advancements in computer theory, led to development in computer science and eventually to AI. With the invention of an electronic means of processing data came a medium that made AI possible. ...

... entering a program easier. Further advancements in computer theory, led to development in computer science and eventually to AI. With the invention of an electronic means of processing data came a medium that made AI possible. ...

Trading Off Solution Quality for Faster Computation in

... document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the sponsoring organizations, agencies, companies or the U.S. government. An earlier version of this paper without the Non-Uniformly Weighted Heuristics Mechanism an ...

... document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the sponsoring organizations, agencies, companies or the U.S. government. An earlier version of this paper without the Non-Uniformly Weighted Heuristics Mechanism an ...

Algorithms and Arguments Artificial Intelligence

... which goes to form a piece of reasoning, which they are capable of performing. For, if we begin from the beginning, that process would involve four tolerably distinct steps. even if human reasoning were based on algorithms, it could not be considered as a mechanical computation: “There is, first, th ...

... which goes to form a piece of reasoning, which they are capable of performing. For, if we begin from the beginning, that process would involve four tolerably distinct steps. even if human reasoning were based on algorithms, it could not be considered as a mechanical computation: “There is, first, th ...

Description Logics

... In their introduction to The Description Logic Handbook [11], Brachman and Nardi point out that the general goal of knowledge representation (KR) is to “develop formalisms for providing high-level descriptions of the world that can be effectively used to build intelligent applications” [32]. This se ...

... In their introduction to The Description Logic Handbook [11], Brachman and Nardi point out that the general goal of knowledge representation (KR) is to “develop formalisms for providing high-level descriptions of the world that can be effectively used to build intelligent applications” [32]. This se ...

A Short Tutorial on Model

... • Tackles the problem of diagnosing a system (a designed artifact), that is, tracking the causes (faults) responsible for a system failure. Usually the input of a diagnosis algorithm is a set of symptoms, i.e. discrepancies between the observed behaviour and the expected one, and its output is a set ...

... • Tackles the problem of diagnosing a system (a designed artifact), that is, tracking the causes (faults) responsible for a system failure. Usually the input of a diagnosis algorithm is a set of symptoms, i.e. discrepancies between the observed behaviour and the expected one, and its output is a set ...

The Consistent Labeling Problem: Part I

... direction and a person traveling along the edge in the angular direction of the edge will always find the darker side of the edge to his right. Low curvature edges mean that the maximum angle by which any small edge segment can bend with respect to its predecessor edge or successor edge segment is l ...

... direction and a person traveling along the edge in the angular direction of the edge will always find the darker side of the edge to his right. Low curvature edges mean that the maximum angle by which any small edge segment can bend with respect to its predecessor edge or successor edge segment is l ...

pdf

... use any one of a number of approximation algorithms. The most well-known approximation heuristic search algorithms are Weighted A* or WA* (Pohl 1970; 1973; Gaschnig 1979; Pearl 1985) and beam search (Bisiani 1987; Winston 1992; Zhou & Hansen 2004). In (Furcy 2004), we showed that beam search scales ...

... use any one of a number of approximation algorithms. The most well-known approximation heuristic search algorithms are Weighted A* or WA* (Pohl 1970; 1973; Gaschnig 1979; Pearl 1985) and beam search (Bisiani 1987; Winston 1992; Zhou & Hansen 2004). In (Furcy 2004), we showed that beam search scales ...

# 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.