EVNSMAS · An evolutionary approach to automated norm synthesis for multi agent systems

An evolutionary approach to automated norm synthesis for multi agent systems

The area of multi-agent systems (MAS) is concerned with the design and implementation of systems where autonomous agents interact, usually to achieve their own goals, and sometimes to solve complex problems that cannot be solved individually. MAS are present in human societies, e.g., . (autonomous) cars in a road, mobile robots in warehouses, autonomous drones (UAVs), etc. Within the MAS domain, a key open problem is that of coordination, i.e., how to manage the interactions between agents. One of the most successful coordination approaches is that of normative sytems, that is, the use of norms that coordinate the agents by specifying what they can and cannot do. Some desirable properties for normative systems are stability and effectiveness, i.e., norms that the agents will comply with (stable), and whose compliance will successfully achieve coordination (effectiveness). Synthesising stable and effective normative systems is a crucial problem for system designers and policy makers. For instance, with the advent of autonomous cars, it will be crucial to design norms that cars will comply with because non-compliance will be prejudicial for them; and whose compliance will avoid undesirable outcomes such as collisions. However, designing norms for MAS can be a highly complex, time consuming and error prone task, specially when the agents may have different goals and preferences. For example, autonomous cars might be made by different companies that establish different driving policies for their cars. For this reason, several approaches have been proposed for the automatic design (synthesis) of normative systems, even though it still remains an open problem. The main goal of this project is to develop a framework for the automatic synthesis of stable and effective normative systems for system designers and policy makers. Our framework roots in the framework of Evolutionary Game Theory (EGT), which provides a mathematical framework and an algorithm for the prediction of stable strategies in MAS. Our goal is to build on the framework of EGT in order to develop: 1. A mathematical framework to model normative systems in MAS from an EGT perspective. 2. Equations and algorithms for the simulation of the evolution of norms in MAS. 3. A computational framework for the automatic synthesis of stable and effective norms. At the end of the action, we successfully achieved the main goal of the project. We developed a framework called SENSE (Synthesis of Evolutionarily stable Normative SystEms) that employs EGT to automatically synthesise stable anf effective. SENSE takes as input descriptions of an agent population and a collection of games modelling different coordination situations, and outputs sets of stable norms that effectively coordinate these agents as required by a policy maker.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз