Details of Grant 

EPSRC Reference:	EP/S011609/1
Title:	Automation and Contemplation for Model Adaptation in Multiagent Interactions
Principal Investigator:	Zeng, Professor Y
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department:	Sch of Computing, Eng & Digital Tech
Organisation:	Teesside University
Scheme:	New Investigator Award
Starts:	01 April 2019	Ends:	31 March 2021	Value (£):	201,328
EPSRC Research Topic Classifications:	Artificial Intelligence
EPSRC Industrial Sector Classifications:	No relevance to Underpinning Sectors
Related Grants:
Panel History:	Panel Date Panel Name Outcome 04 Jul 2018 EPSRC ICT Prioritisation Panel July 2018 Announced
Summary on Grant Application Form
An agent is a computer system that acts intelligently given its sensory input from the environment. Agent technologies have proved to be effective and reliable solutions in many practical applications and will continue to play a major role in modern society. For example, the eBay buyer agent recommends good deals for people in an e-market. The Google self-driving car operated by an autonomous agent has successfully navigated thousands of miles on the road. A smart meter controlled by an intelligent software agent helps optimize energy consumption for a household. In many such applications, an autonomous agent (namely a subject agent) is expected to make a rational decision by predicting behaviors of other agents in a common environment. The decision quality relies on building decision models of the other agents and then solving the models to understand how the other agents will behave in the environment. When the subject agent's model is deployed in a real-world application, it may fail since the subject agent may receive unexpected observations incurred by other agents. Hence the challenge is about the prediction of other agents' behavior and the interpretation of model failure so as to adapt the subject agent's model for successful interactions. The goal of this project is to improve the subject agent's adaptation by automating the model construction of other agents and revising its own decision model when the model fails in the execution. This project will propose scalable learning algorithms to build decision models of other agents upon historical data of agents' interactions. The algorithms will also facilitate the model construction in a new problem domain that will be likely larger and more uncertain in practice. To interpret failures of the subject agent's decision model, this project will search for a novel reasoning technique to identify the most probable reasons behind the failures, and accordingly revise the model so that the subject agent's decisions can be adapted to the other agents' behaviors in their real-time interactions. This project will implement all the proposed techniques in a toolkit and conduct comprehensive tests to evaluate practical utilities of the toolkit. Real-world applications on personalized learning and intelligent computer game AI engine development will be extended through our industrial collaborators. The broader impact of this research will be to enable individual agents to act rationally in complex multiagent environments. This is a crucial step toward the integration of autonomous agent technology within society that will support humans in tasks such as disaster response, energy distribution and security operation.
Key Findings
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Potential use in non-academic contexts
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