Adaptable and Verifiable BDI Reasoning

TL;DR

This paper proposes a system architecture for BDI autonomous agents that can adapt to environmental changes by maintaining self-models, learning new actions, and reasoning about durative actions, addressing the challenge of long-term autonomy.

Contribution

It introduces a novel architecture for BDI agents with self-models and theories for durative actions and learning, enabling adaptation in dynamic environments.

Findings

Proposed a self-model architecture for BDI agents

Outlined theories for durative actions in BDI systems

Identified research directions for adaptive autonomous agents

Abstract

Long-term autonomy requires autonomous systems to adapt as their capabilities no longer perform as expected. To achieve this, a system must first be capable of detecting such changes. In this position paper, we describe a system architecture for BDI autonomous agents capable of adapting to changes in a dynamic environment and outline the required research. Specifically, we describe an agent-maintained self-model with accompanying theories of durative actions and learning new action descriptions in BDI systems.