Multiplayer Games and Competitive Business Models

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 AI agent computing business bases defined during the last several years can be applied to present precise decision strategies on multiplayer games with only perfect information between agent pairs. A new basis for business modelling with the new agent computing paradigms is essential to economic agent models. Multiplayer agnet game trees are introuced as the basis to ecenomic games The game trees are applied to attain 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 OROperations Research as goal satsfibility and micro-managing decision support with means-end analysis.