A rapid learning and problem solving method: Application to the StarCraft game environment

Abstract

Building on a paradigm of rapid causal learning and problem solving for the purpose of creating adaptive general intelligent systems and autonomous agents that we have reported previously, we report in this paper improved methods of rapid learning of causal rules that are robust and applicable to a wide variety of general situations. The robust rapid causal learning mechanism is also applied to the rapid learning of scripts — knowledge structures that encode extended sequences of actions with certain intended outcomes and goals. Our method requires only a small number of training instances for the learning of basic causal rules and scripts. We demonstrate, using the StarCraft game environment, how scripts can vastly accelerate problem solving processes and obviate the need for computationally expensive and relatively blind search processes. Our system exhibits human-like intelligence in terms of the rapid learning of causality and learning and packaging of knowledge in increasingly larger chunks in the form of scripts for accelerated problem solving.