A Future for Knowledge Acquisition: 8th European Knowledge by Luc Steels, Guus Schreiber, Walter Van de Velde PDF

By Luc Steels, Guus Schreiber, Walter Van de Velde

This quantity includes a range of the main papers provided on the 8th eu wisdom Acquisition Workshop (EKAW '94), held in Hoegaarden, Belgium in September 1994.
The ebook demonstrates that paintings within the mainstream of data acquisition results in precious sensible effects and places the data acquisition firm in a broader theoretical and technological context. The 21 revised complete papers are rigorously chosen key contributions; they deal with wisdom modelling frameworks, the id of conventional elements, method facets, and architectures and functions. the quantity opens with a considerable preface by way of the quantity editors surveying the contents.

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Extra info for A Future for Knowledge Acquisition: 8th European Knowledge Acquisition Workshop, EKAW '94 Hoegaarden, Belgium, September 26–29, 1994 Proceedings

Sample text

Roughly speaking, this is the role of knowledge representation in AI generally: The intended role of knowledge representation in artificial intelligence is reduce problems of intelligent action to search problems. Why is it that using a formal language to describe the knowledge needed to solve some problem can be expected to transform that problem into one involving search? 3. Imagine that instead of viewing this as a search problem, we try to prove that it is possible for us to get the light installed.

Because there are typically many ways in which rules of inference can be applied in any particular situation, deduction is a search problem. The initial node is the information with which the system is supplied, and the goal nodes are those in which the desired conclusion has been derived. The operators that generate the successors of a given node are those that draw a new conclusion by applying some rule of inference. Viewing inference as search suggests that we reexamine some of the observations of the previous section.

9. By expanding the nodes in this optimal order, we minimize the amount of time needed to solve the problem. Heuristic search is an attempt to search a tree in an order that is more nearly optimal than either breadth-first or depth-first. Roughly speaking, when we pick a node from the list L in step 2 of the search procedure, what we would like to do is to move steadily from the root node toward the goal by always selecting a node that is as close to the goal as possible. In some domains, it is possible to estimate the distance to the goal and to use this information to guide the search.

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