The best current programs for two-person perfect information games play only a single game; they search exhaustively and they do not learn. The goals of this project are to demonstrate further the viability of a weak theory as a search paradigm and to facilitate the construction of computers that learn entire categories of tasks, rather than requiring individualized instruction. This research develops a program, HOYLE, that can play any such game correctly and, with experience, can learn to play it extremely well. Instead of extensive search, HOYLE learns a search control strategy for each new game under the guidance of its weak domain theory: a combination of procedural knowledge about game playing, declarative knowledge about specific games, a language and framework for strategic elements, and a set of narrow but expert perspectives called Advisors. As it plays a new game, HOYLE selectively constructs, organizes, and reformulates a knowledge base for each game from its playing experience. Under a novel architecture that employs a variety of control strategies for move selection, HOYLE combines that knowledge base with its Advisors to produce a coherentr, incisive, steadily improving strategy for the game. A prototype of this multifaceted learning has proved itself remarkably effective in a varied but limited domain. HOYLE addresses, both in its design and in its implementation, important questions in theory formation, collaboratin of experts, conflict resolution, experimental design, and operationalization.
