Learning, Innovation, and Explanation in Self-Organising Multi-Agent Systems

The initial research proposal focuses on learning, innovation, and explanation in multi-agent systems. Three levels have been identified concerning the design of multi-agent systems: the agent (individual) level, the inter-agent (social) level, and the system level. Previous work has proposed socially-inspired mechanisms to promote collective action and self-organisation at the inter-agent level and GP-based techniques for adaptation and approximate optimisation at the agent and system levels. We would like to explore learning and innovation at all three levels as ways to handle potentially dynamic changes in the environment and ensure sustainability.

We note that the methods which can be applied to the agent level, such as reinforcement learning for policy optimisation, are the same sort of methods which can be applied to the system level if we consider the system entity as a control agent. So far, our research has focused on constructing a single system-wide policy by which all agents must abide. However, in a polycentric perspective, the agents should be able to formulate their own policies and plans of action and to participate in the formulation of system policies. This would require mechanisms for combining different sensory experiences and opinions, using communication and knowledge transfer protocols in a collective learning fashion in order to achieve social learning and social innovation.

The research question we propose at this stage is: how should individual and collective system policies be formulated and explained so as to sustainably improve and maintain system performance and agent satisfaction, even in dynamic environments?

Formulation refers both to the distributed learning algorithms and protocols which enable the agents to construct individual and collective policies and to the way the policies are represented. Explainability refers to the intelligibility of both the learning algorithms which result in the creation of new policies and the policies themselves. Understanding why a policy has been selected and why it is better than other candidate policies which could also have been selected is important for system designers and users to be able to trust its recommendations and gather valuable knowledge regarding the problem domain. Some learning models are inherently intelligible while others are not and it is possible that some unintelligible models can be made understandable, explainable, and interpretable. Intelligible models could shed a new light upon problem domains which are intrinsically hard for humans to understand and lead to the adoption of new and better strategies for searching the theoretically infinite space of sets of rules upon which policies are built.

At this stage we have identified the following high-level tasks:
- Review previous work on the design of multi-agent systems, focusing on the three levels identified above: the agent, the social, and the system levels
- Identify knowledge gaps in previous work, namely regarding polycentric construction of policies and explainability
- Write survey paper based on both the literature review and the conclusions about knowledge gaps
- Identify possible applications and systems to use as running examples for policy construction and selection
- Iteratively propose solutions to three levels of policy construction: single system policy (current work); multiple agent policies; multiple agent policies and a collectively formulated system policy

This project aims at proposing a new paradigm for the design of collective adaptive systems in which system policies are not handcrafted and designers do not hardcode immutable rule sets, but rather the result of automatic construction with the explicit goal of improving system performance and agent satisfaction.

EPSRC Research Areas: Control Engineering, Engineering design, Artificial intelligence technologies, Software engineering