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Open Access Publications from the University of California

Integrating Learning in a Multi-Scale Agent

  • Author(s): Weber, Ben
  • Advisor(s): Mateas, Michael
  • Jhala, Arnav
  • et al.
Abstract

Video games are complex simulation environments with many real-world properties that need to be addressed in order to build robust intelligence. StarCraft is a real-time strategy (RTS) game that exhibits both cognitive complexity and task environment complexity. Expert StarCraft gameplay involves many cognitive processes including estimation, anticipation, and adaptation. Achieving the objective of destroying all enemy forces requires managing a number of concurrent subtasks while working towards higher-level objectives. Working towards the goal of building expert-level performance for RTS games presents a multi-scale AI problem, which motivates the need for integrative AI systems.

This thesis investigates the capabilities necessary to realize expert StarCraft gameplay in an agent. My central claim is that in order to perform at the level of an expert player, a StarCraft agent must utilize heterogeneous reasoning capabilities. This requirement is motivated by the structure of RTS gameplay, which involves both deliberative and reactive decision making, and analysis of professional gameplay, which demonstrates the need for estimation, adaptation, and anticipation reasoning capabilities. Additionally, StarCraft gameplay involves decision making across multiple scales, or levels of coordination. My approach for supporting these capabilities in an agent is to identify the competencies necessary for RTS gameplay, and develop techniques for implementing and integrating these competencies. The resulting agent, EISBot, integrates reactive planning for plan execution and monitoring, machine learning for opponent modeling, and case-based reasoning for goal formulation and strategy learning. EISBot plays StarCraft at the same action and sensing granularity as human players, and is evaluated against AI and human opponents.

The contributions of this thesis are idioms for authoring agents for multi-scale AI problems, techniques for learning domain knowledge from gameplay demonstrations, and methods for integrating a variety of learning algorithms in a real-time, multi-scale agent.

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