Verifiable Self-Aware Agent-Based Autonomous Systems

In this article, we describe an approach to autonomous system construction that not only supports self-awareness but also formal verification. This is based on modular construction where the key autonomous decision making is captured within a symbolically described "agent." So, this articl...

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Bibliographic Details
Published in:Proceedings of the IEEE Vol. 108; no. 7; pp. 1011 - 1026
Main Authors: Dennis, Louise A., Fisher, Michael
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Autonomous agents
Autonomous automobiles
Autonomous systems
Autonomy
Computer architecture
Decision making
Drones
formal verification
Modular construction
Modular systems
Programming
robot programming
Robotics
Robots
Safety
Self awareness
Testing
Verification
ISSN:0018-9219, 1558-2256
Online Access:Get full text
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Summary:In this article, we describe an approach to autonomous system construction that not only supports self-awareness but also formal verification. This is based on modular construction where the key autonomous decision making is captured within a symbolically described "agent." So, this article leads us from traditional systems architectures, via agent-based computing, to explainability, reconfigurability, and verifiability, and on to applications in robotics, autonomous vehicles, and machine ethics. Fundamentally, we consider self-awareness from an agent-based perspective. Agents are an important abstraction capturing autonomy, and we are particularly concerned with intentional, or rational, agents that expose the "intentions" of the autonomous system. Beyond being a useful abstract concept, agents also provide a practical engineering approach for building the core software in autonomous systems such as robots and vehicles. In a modular autonomous system architecture, agents of this form capture important decision making elements. Furthermore, this ability to transparently capture such decision making processes, and especially being able to expose their intentions, within an agent allows us to apply strong (formal) agent verification techniques to these systems.
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ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2020.2991262