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A key element in the solution of reinforcement learning problems is the value function The purpose of this function is to measure the long term utility or value of any given state The function is important because an agent can use this measure to decide what to do next A common problem in reinforcement learning when applied to systems having continuous(More)
Artificial Intelligence techniques have been successfully applied to several computer games. However in some kinds of computer games, like real-time strategy (RTS) games, traditional artificial intelligence techniques fail to play at a human level because of the vast search spaces that they entail. In this paper we present a real-time case based planning(More)
Case-based reasoning systems have traditionallybeen used to perform high-level reasoning in problem domains that can be adequately described using discrete, symbolic representations. However, many realworld problem domains, such as autonomous robotic navigation, are better characterized using continuous representations. Such problem domains also require(More)
This paper explores the application of genetic algorithms to the learning of local robot navigation behaviors for reactive control systems. Our approach evolves reactive control systems in various environments, thus creating sets of \ecological niches" that can be used in similar environments. The use of genetic algorithms as an unsupervised learning method(More)
Some domains, such as real-time strategy (RTS) games, pose several challenges to traditional planning and machine learning techniques. In this paper, we present a novel on-line case-based planning architecture that addresses some of these problems. Our architecture addresses issues of plan acquisition, on-line plan execution, interleaved planning and(More)
Computer games are an increasingly popular application for Artificial Intelligence (AI) research, and conversely AI is an increasingly popular selling point for commercial games. Although games are typically associated with entertainment, there are many “serious” applications of gaming, including military, corporate, and advertising applications. There are(More)
Feature selection has proven to be a valuable technique in supervised learning for improving predictive accuracy while reducing the number of attributes considered in a task. We investigate the potential for similar benefits in an unsupervised learning task, conceptual clustering. The issues raised in feature selection by the absence of class labels are(More)
This article describes how a reasoner can improve its understanding of an incompletely understood domain through the application of what it already knows to novel problems in that domain. Case-based reasoning is the process of using past experiences stored in the reasoner's memory to understand novel situations or solve novel problems. However, this process(More)
In this paper we begin to investigate how to <i>automatically</i> determine the subjectivity orientation of questions posted by real users in community question answering (CQA) portals. Subjective questions seek answers containing private states, such as personal opinion and experience. In contrast, objective questions request objective, verifiable(More)
While plan recognition research has been applied to a wide variety of problems, it has largely made identical assumptions about the number of agents participating in the plan, the observability of the plan execution process, and the scale of the domain. We describe a method for plan recognition in a real-world domain involving large numbers of agents(More)