Embracing AWKWARD! A Hybrid Architecture for Adjustable Socially-Aware Agents

Sammanfattning: This dissertation presents AWKWARD: a hybrid architecture for the development of socially aware agents in Multi-Agent Systems (MAS). AWKWARD bridges Artificial Intelligence (AI) methods for their individual and combined strengths; Behaviour Oriented Design (BOD) is used to develop reactive planning agents, the OperA framework is used to modeland validate agent behaviour as per social norms, and Reinforcement Learning (RL) is used to optimise plan structures that induce desirable social outcomes. In concert, OperA and BOD help AWKWARD agents achieve real-time adjustment of reactive plans in response to social obligations. As systems scale, however, reactive plans become challenging to optimise by hand. In this work, AWKWARD’s extensibility is demonstrated to tackle this problem. The RL module is presented as an additional AI method that enables the automatic restructuring of reactive plans. A sample implementation of AWKWARD is developed in DOTA2—a game where success is heavily dependent on social interactions. The results gathered demonstrate the social outcome achieved from plan adjustments in real time, in both experiments of norm enforcement using OperA, and experiments of norm reinforcement using the extended RL module. Each sub-component, including the reactive planner itself, is a decision-making entity that dictates agent behaviour at various stages of the system’s life cycle. The level of decision-making control of each module is adjusted at various stages, making AWKWARD a system with Variable Autonomy (VA). The concept of VA isdiscussed as one that can aid in maintaining human control over a system. However, as with any VA system, challenges of transparency and transfer of control, amongst others, present themselves. Suggestions for tackling these challenges are presented as next steps for the maturation of AWKWARD as a platform, and the DOTA2 implementation as a research and educational platform.