Ontology-Enhanced Decision-Making for Autonomous Agents in Dynamic and Partially Observable Environments

TL;DR

This paper presents OntoDeM, an ontology-based decision-making model that enhances autonomous agents' ability to interpret unforeseen events, adapt goals, and operate effectively in dynamic, partially observable environments, outperforming traditional methods.

Contribution

The paper introduces OntoDeM, a novel ontology-enhanced decision-making framework that improves real-time perception and goal adaptation for autonomous agents in complex settings.

Findings

OntoDeM improves agents' perception accuracy in real-world scenarios.

The model enables adaptive goal generation in unforeseen situations.

OntoDeM outperforms traditional RL algorithms in dynamic environments.

Abstract

Agents, whether software or hardware, perceive their environment through sensors and act using actuators, often operating in dynamic, partially observable settings. They face challenges like incomplete and noisy data, unforeseen situations, and the need to adapt goals in real-time. Traditional reasoning and ML methods, including Reinforcement Learning (RL), help but are limited by data needs, predefined goals, and extensive exploration periods. Ontologies offer a solution by integrating diverse information sources, enhancing decision-making in complex environments. This thesis introduces an ontology-enhanced decision-making model (OntoDeM) for autonomous agents. OntoDeM enriches agents' domain knowledge, allowing them to interpret unforeseen events, generate or adapt goals, and make better decisions. Key contributions include: 1. An ontology-based method to improve agents' real-time observations using prior knowledge. 2. The OntoDeM model for handling dynamic, unforeseen situations by evolving or generating new goals. 3. Implementation and evaluation in four real-world applications, demonstrating its effectiveness. Compared to traditional and advanced learning algorithms, OntoDeM shows superior performance in improving agents' observations and decision-making in dynamic, partially observable environments.