Titlebook: Advances in Artificial Intelligence; 15th Conference of t Robin Cohen,Bruce Spencer Conference proceedings 2002 Springer-Verlag Berlin Heid

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https://doi.org/10.1007/978-1-4615-8155-0ncludes two types of mobile agents - consultants and specialists. When an agent system calls in AERO to help it with its exception handling, a consultant migrates to the host system and in turn calls in a number of specialists, who also migrate to the host system to handle to the required types of e

Lumbar-Stenosis

The Galton-Watson Process: Analytic Methodsake into account the fact that multiple selling agents may offer the same good with different qualities. In our approach, buying agents learn to avoid the risk of purchasing low quality goods and to maximize their expected value of goods by dynamically maintaining sets of reputable sellers. Selling

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Krishna B. Athreya,Peter E. Neyally, a grid is superimposed over a region, and a graph search is used to find the optimal (minimal cost) path. The most common scenario is to use a grid of tiles and to search using A*. This paper discusses the tradeoffs for different grid representations and grid search algorithms. Grid representa

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Multi-Type Branching Processes,e effective in the single-agent domain. This paper presents TDSAB, applying the ideas behind TDS parallelism to the MTD(f) algorithm. Results show that TDSAB gives comparable performance to that achieved by conventional parallel αβ algorithms. This result is very encouraging, given that traditional

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Jean-Pierre Dion,Belkheir Essebbarblems is to learn the input-output functionality from examples. However, in such situations it is not known which information is relevant to the task at hand. In this paper we focus on selection of relevant examples. We propose a new noise elimination method which is based on the filtering of the so

essential-fats

https://doi.org/10.1007/978-3-642-52115-7e of inductive bias. This inductive bias enables TRM to generate more accurate hypotheses for new tasks that have small sets of training examples. TRM has a knowledge retention phase during which the neural network representation of a successfully learned task is stored in a domain knowledge databas

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Lecture Notes in Biomathematics combinatorial problems. In such a language one writes function-free logic programs with negation. Such a program is instantiated to a ground program and its stable models are computed. In this paper, we identify a class of logic programs for which the current techniques in solving SAT problems can

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