A stochastically verifiable autonomous control architecture with reasoning

TL;DR

This paper introduces LISA, a simplified agent architecture enabling probabilistic verification through automatic compilation into DTMC and MDP models, supporting both design-time and run-time verification for autonomous control.

Contribution

LISA is a novel, structurally simpler agent architecture that facilitates formal probabilistic verification by automatic model generation from agent programs.

Findings

LISA can be automatically compiled into DTMC and MDP models.

The models support complete probabilistic verification of the agent and environment.

Run-time verification is feasible using internal probabilistic models.

Abstract

A new agent architecture called Limited Instruction Set Agent (LISA) is introduced for autonomous control. The new architecture is based on previous implementations of AgentSpeak and it is structurally simpler than its predecessors with the aim of facilitating design-time and run-time verification methods. The process of abstracting the LISA system to two different types of discrete probabilistic models (DTMC and MDP) is investigated and illustrated. The LISA system provides a tool for complete modelling of the agent and the environment for probabilistic verification. The agent program can be automatically compiled into a DTMC or a MDP model for verification with Prism. The automatically generated Prism model can be used for both design-time and run-time verification. The run-time verification is investigated and illustrated in the LISA system as an internal modelling mechanism for prediction of future outcomes.