Sequential instance -based learning for planning in the context of an imperfect information game.

Title: Sequential instance -based learning for planning in the context of an imperfect information game.
Identifier: AAI9969733
identifier: 9969733
Creator: Shih, Jenngang.
Contributor: Adviser: Susan L. Epstein
Date: 2000
Language: English
Publisher: City University of New York.
Subject: Computer Science | Artificial Intelligence
Abstract: Finding sequential concepts, as in planning, is a complex task because of the exponential size of the search space. Empirical learning is an effective way to find sequential concepts from observations. Sequential Instance-Based Learning (SIBL), which is presented here, is an empirical learning approach, modeled after Instance-Based Learning (IBL), that learns sequential concepts, ordered sequences of state-action pairs to perform a synthesis task. SIBL is highly effective; it learns expert-level knowledge. SI13L demonstrates the feasibility of using an empirical learning approach to discover sequential concepts. In addition, this approach suggests a general framework that systematically extends empirical learning to learning sequential concepts. In this dissertation, SIBL is tested on the domain of bridge.
Type: dissertation
Source: PQT Legacy CUNY.xlsx
degree: Ph.D.

Media: Sequential instance -based learning for planning in the context of an imperfect information game.