Adaptable and Verifiable BDI Reasoning
Peter Stringer (University of Liverpool), Rafael C. Cardoso, (University of Liverpool), Xiaowei Huang (University of Liverpool), Louise A., Dennis (University of Liverpool)

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.
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Taxonomy
TopicsSemantic Web and Ontologies
