Modelling intelligent agents through causality theory

Abstract

We introduce Causal Agents, a methodology and agent architecture for modeling intelligent agents based on Causality Theory. We draw upon concepts from classical philosophy about metaphysical causes of existing entities for defining agents in terms of their formal, material, efficient and final causes and use computational mechanisms from Bayesian causal models for designing causal agents. Agent's intentions, interactions and performance are governed by their final causes. A Semantic Bayesian Causal Model, which integrates a probabilistic causal model with a semantic layer, is used by agents for knowledge representation and inference. Agents are able to use semantic information from external stimuli (utterances, for example) which are mapped into the agent's causal model for reasoning about causal relationships with probabilistic methods. Our theory is being tested by an operational multiagents system implementation for managing research products.