Reaching the Goal in Real-Time Heuristic Search: Scrubbing
... movement (Koenig 2001). This well-studied problem has led to numerous algorithms (Korf 1990; Furcy and Koenig 2000; Shimbo and Ishida 2003; Hernández and Meseguer 2005; Bulitko and Lee 2006; Hernández and Baier 2012) and various theoretical analysis (Ishida and Korf 1991; Koenig and Simmons 1993; ...
... movement (Koenig 2001). This well-studied problem has led to numerous algorithms (Korf 1990; Furcy and Koenig 2000; Shimbo and Ishida 2003; Hernández and Meseguer 2005; Bulitko and Lee 2006; Hernández and Baier 2012) and various theoretical analysis (Ishida and Korf 1991; Koenig and Simmons 1993; ...
Dynamic NMFs with Temporal Regularization for Online Analysis of
... such as those that arise in social media. By constructing such representations, “signal” can be separated from “noise” and essential data characteristics can be continuously summarized in terms of a small number of human interpretable components. In the context of social media applications, this map ...
... such as those that arise in social media. By constructing such representations, “signal” can be separated from “noise” and essential data characteristics can be continuously summarized in terms of a small number of human interpretable components. In the context of social media applications, this map ...
A Stochastic Algorithm for Feature Selection in Pattern Recognition
... its subsets since many problems related to feature extraction have been shown to be NP-hard (Blum and Rivest, 1992). Therefore, automatic feature space construction and variable selection from a large set has become an active research area. For instance, in Fleuret and Geman (2001), Amit and Geman ( ...
... its subsets since many problems related to feature extraction have been shown to be NP-hard (Blum and Rivest, 1992). Therefore, automatic feature space construction and variable selection from a large set has become an active research area. For instance, in Fleuret and Geman (2001), Amit and Geman ( ...
Predicting Classifier Combinations
... tion can be compared to the ground-truth information. Since our meta-learning approach is a classification task, typically classification measures such as classification accuracy might be used to evaluate the performance of the prediction model. However, this would lead to the following issues: If m ...
... tion can be compared to the ground-truth information. Since our meta-learning approach is a classification task, typically classification measures such as classification accuracy might be used to evaluate the performance of the prediction model. However, this would lead to the following issues: If m ...
Preference Learning: An Introduction
... terminology for the most important types of ranking problems, which will also serve as a guideline for organizing the chapters of the book. Aiolli & Sperduti give an alternative unifying framework for learning to rank from preferences. In general, a preference learning task consists of some set of i ...
... terminology for the most important types of ranking problems, which will also serve as a guideline for organizing the chapters of the book. Aiolli & Sperduti give an alternative unifying framework for learning to rank from preferences. In general, a preference learning task consists of some set of i ...
powerpoint slides
... • In the earliest ES’s (where only a single solution was maintained), the new individual replaced its parent if it had a higher fitness • In addition, these early ES’s, maintained the same value for σ throughout the duration of the algorithm • It has been proven that if this vector remains constant ...
... • In the earliest ES’s (where only a single solution was maintained), the new individual replaced its parent if it had a higher fitness • In addition, these early ES’s, maintained the same value for σ throughout the duration of the algorithm • It has been proven that if this vector remains constant ...
The steady-state control problem for Markov decision processes
... Probabilistic systems are frequently modeled as Markov chains, which are composed of a set of states and a probabilistic transition relation specifying the probability of moving from one state to another. When the system interacts with the environment, as is very often the case in real-life applicat ...
... Probabilistic systems are frequently modeled as Markov chains, which are composed of a set of states and a probabilistic transition relation specifying the probability of moving from one state to another. When the system interacts with the environment, as is very often the case in real-life applicat ...
Disco – Novo – GoGo Meinolf Sellmann Carlos Ans´otegui
... the branching variables randomly while trying to learn good value heuristics over the course of different restarts. The motivation for this is that a good value selection heuristic can guide us to a feasible solution effectively no matter how badly we happen to partition the search space. The idea i ...
... the branching variables randomly while trying to learn good value heuristics over the course of different restarts. The motivation for this is that a good value selection heuristic can guide us to a feasible solution effectively no matter how badly we happen to partition the search space. The idea i ...
Computing Contingent Plans via Fully Observable
... generally easier to compute since integrating online sensing with planning eliminates the need to plan for a potentially exponential (in the size of relevant unknown facts) number of contingencies. In the absence of deadends, online contingent planning can be fast and effective. Recent advances incl ...
... generally easier to compute since integrating online sensing with planning eliminates the need to plan for a potentially exponential (in the size of relevant unknown facts) number of contingencies. In the absence of deadends, online contingent planning can be fast and effective. Recent advances incl ...
Modular Basic Action Theories - Department of Computer Science
... Modular BAT The SC and BAT are able to specify the evolution of dynamic systems in a very natural way. However, in practice it is not easy to specify a dynamic system with very large number of actions. If a system involves hundreds or even thousands of actions, it will be difficult to specify BAT. T ...
... Modular BAT The SC and BAT are able to specify the evolution of dynamic systems in a very natural way. However, in practice it is not easy to specify a dynamic system with very large number of actions. If a system involves hundreds or even thousands of actions, it will be difficult to specify BAT. T ...
Relational Learning as Search in a Critical Region
... • A low generalization error of a learned hypothesis does not imply that the “true” target concept has been captured. This is particularly important for automated knowledge discovery, where a major issue is to provide experts with new relevant insights into the domain under analysis. As a matter of ...
... • A low generalization error of a learned hypothesis does not imply that the “true” target concept has been captured. This is particularly important for automated knowledge discovery, where a major issue is to provide experts with new relevant insights into the domain under analysis. As a matter of ...
Solution Manual Artificial Intelligence a Modern Approach
... acting. Essentially any object qualifies; the key point is the way the object implements an agent function. (Note: some authors restrict the term to programs that operate on behalf of a human, or to programs that can cause some or all of their code to run on other machines on a network, as in mobile ...
... acting. Essentially any object qualifies; the key point is the way the object implements an agent function. (Note: some authors restrict the term to programs that operate on behalf of a human, or to programs that can cause some or all of their code to run on other machines on a network, as in mobile ...
New approaches for heuristic search: linkage with artificial
... in solving hard problems with heuristic search. That is the focus here. But longstanding methods of directed tree search with classical problem heuristics, such as for the Traveling Salesman P r o b l e m - - a paradigm for combinatorially difficult p r o b l e m s - - a r e not wholly satisfactory. ...
... in solving hard problems with heuristic search. That is the focus here. But longstanding methods of directed tree search with classical problem heuristics, such as for the Traveling Salesman P r o b l e m - - a paradigm for combinatorially difficult p r o b l e m s - - a r e not wholly satisfactory. ...
Intelligent Agents. - Home ANU
... The performance measure evaluates the environment sequence A rational agent maximizes expected performance Agent programs implement agent functions Environments are categorized along several dimensions: observable? deterministic? episodic? static? discrete? single-agent? ...
... The performance measure evaluates the environment sequence A rational agent maximizes expected performance Agent programs implement agent functions Environments are categorized along several dimensions: observable? deterministic? episodic? static? discrete? single-agent? ...
The complexity of planning - Dartmouth Computer Science
... be computationally much simpler, since in this case rewards cannot cancel each other out. We abstract the policy existence problems as follows: “Is there a policy with expected reward > 0?” We have chosen these decision problems because they can be used, along with binary search, to calculate the ex ...
... be computationally much simpler, since in this case rewards cannot cancel each other out. We abstract the policy existence problems as follows: “Is there a policy with expected reward > 0?” We have chosen these decision problems because they can be used, along with binary search, to calculate the ex ...
CTL AgentSpeak(L): a specification language for agent programs
... If the agent ag and his circumstance C are explicit, we simply write BEL(φ), DES(φ), and INTEND(φ). So an agent ag is said to believe the atomic formula φ, if φ is a logical consequence of the beliefs bs of ag. An agent is said to intend the atomic formula φ, if φ is the subject of an achieve goal i ...
... If the agent ag and his circumstance C are explicit, we simply write BEL(φ), DES(φ), and INTEND(φ). So an agent ag is said to believe the atomic formula φ, if φ is a logical consequence of the beliefs bs of ag. An agent is said to intend the atomic formula φ, if φ is the subject of an achieve goal i ...
Stochastic dominance-constrained Markov decision processes
... reward, such as long-run average reward or discounted reward. The variation/spread/ dispersion of policies is also critical to their evaluation. Given two policies with equal expected performance, we would prefer the one with smaller variation in some sense. Consider a discounted portfolio optimizat ...
... reward, such as long-run average reward or discounted reward. The variation/spread/ dispersion of policies is also critical to their evaluation. Given two policies with equal expected performance, we would prefer the one with smaller variation in some sense. Consider a discounted portfolio optimizat ...
artificial intelligence - cs2302 computer networks
... An omniscient agent knows the actual outcome of its actions and can act accordingly; but omniscience is impossible in reality. Doing actions in order to modify future percepts-sometimes called information gathering-is an important part of rationality. Our definition requires a rational agent not onl ...
... An omniscient agent knows the actual outcome of its actions and can act accordingly; but omniscience is impossible in reality. Doing actions in order to modify future percepts-sometimes called information gathering-is an important part of rationality. Our definition requires a rational agent not onl ...
1.1.1 What is artificial intelligence?
... An omniscient agent knows the actual outcome of its actions and can act accordingly; but omniscience is impossible in reality. Doing actions in order to modify future percepts-sometimes called information gathering-is an important part of rationality. Our definition requires a rational agent not onl ...
... An omniscient agent knows the actual outcome of its actions and can act accordingly; but omniscience is impossible in reality. Doing actions in order to modify future percepts-sometimes called information gathering-is an important part of rationality. Our definition requires a rational agent not onl ...
An Integrated Approach of Learning, Planning, and Execution
... Simmons and Mitchell, 1989; Stone and Veloso, 1998; Matellán et al., 1998; Ashish et al., 1997), ranging from work on autonomous robotic agents to Web-based software agents. It integrates many areas, such as robotics, planning, and machine learning. This integration opens many questions that arise w ...
... Simmons and Mitchell, 1989; Stone and Veloso, 1998; Matellán et al., 1998; Ashish et al., 1997), ranging from work on autonomous robotic agents to Web-based software agents. It integrates many areas, such as robotics, planning, and machine learning. This integration opens many questions that arise w ...