Multiagent Games, Competitive Models, and Game Tree Planning

Contents

The paper is based on agent plan computing where the interaction amongst heterogeneous computing resources is via objects, multiagent AI and agent intelligent languages. Modeling, objectives, and planning issues are examined at an agent planning. A basis to model discovery and prediction planning is stated. The new agent computing theories the author defined since 1994 can be applied to present precise decision strategies on multiplayer games with only perfect information between agent pairs. The game trees are applied to train models. The computing model is based on a novel competitive learning with agent multiplayer game tree planning. Specific agents are assigned to transform the models to reach goal plans where goals are satisfied based on competitive game tree learning. The planning applications include OR- Operations Research as goal satisfiability and micro-managing decision support with means-end analysis.