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Towards Explainable Agency in Multi-Agents Systems Using Inductive Logic Programming and Answer Set Programming

EasyChair Preprint no. 5465

10 pagesDate: May 4, 2021

Abstract

Logical reasoning is a fundamental aspect of human behaviour, and this is an important criteria to build human-like reasoning in intelligent autonomous multi-agents. So far, the field knowledge representation and reasoning have employed logic-based symbolic techniques to mimic the challenging task of incorporating human-like reasoning in multi-agent systems. However, the field of machine learning has shown increasing in- terest to take on this challenge. In this research, we describe a methodology which is based on Inductive Logic Programming and Answer Set Programming that enables autonomous agents to generate explanations and logic-based reasoning in a form hypothesis from a rich knowledge base (ontologies). Whilst this preliminary work addresses key limitations such as scalability and adaptability, we strongly emphasise the need for logic- based reasoning in multi-agents for interpretability and transparency in their behaviour.

Keyphrases: Answer Set Programming ·, Explainability, Inductive Logic Programming, logic-based reasoning, multi-agent systems, preference learning, Symbolic AI

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:5465,
  author = {Minal Suresh Patil and Kary Främling},
  title = {Towards Explainable Agency in Multi-Agents Systems Using Inductive Logic Programming and Answer Set Programming},
  howpublished = {EasyChair Preprint no. 5465},

  year = {EasyChair, 2021}}
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