This paper presents the AWKWARD architecture for the development of hybrid agents in Multi-Agent Systems. AWKWARD agents can have their plans re-configured in real time to align with social role requirements under changing environmental and social circumstances. The proposed hybrid architecture makes use of Behaviour Oriented Design (BOD) to develop agents with reactive planning and of the well-established OperA framework to provide organisational, social, and interaction definitions in order to validate and adjust agents' behaviours. Together, OperA and BOD can achieve real-time adjustment of agent plans for evolving social roles, while providing the additional benefit of transparency into the interactions that drive this behavioural change in individual agents. We present this architecture to motivate the bridging between traditional symbolic- and behaviour-based AI communities, where such combined solutions can help MAS researchers in their pursuit of building stronger, more robust intelligent agent teams. We use DOTA2, a game where success is heavily dependent on social interactions, as a medium to demonstrate a sample implementation of our proposed hybrid architecture.

Most autonomous robots are equipped with restricted, unreliable, and inaccurate sensors and effectors and operate in complex and dynamic environments. A successful approach to deal with the resulting uncertainty is the use of controllers that prescribe the robots' behavior in terms of concurrent reactive plans (CRPs) -- plans that specify how the robots are to react to sensory input in order to accomplish their jobs reliably (e.g., McDermott, 1992a; Beetz, 1999). Reactive plans are successfully used to produce situation specific behavior, to detect problems and recover from them automatically, and to recognize and exploit opportunities (Beetz et al., 2001). These kinds of behaviors are particularly important for autonomous robots that have only uncertain information about the world, act in dynamically changing environments, and are to accomplish complex tasks efficiently. Besides reliability and flexibility, foresight is another important capability of competent autonomous robots (McDermott, 1992a).

Rather than begin by discussing the points where I seem to be in disagreement with Chapman and Schoppers, let me start with something about which we seem to concur: The work on reactive plans can be broken into two parts. First is the work on pure reac-tive plans, which specify actions for an agent to take in all situations. Second is the work on caching reactive plans, which specify actions in only some instances and are primarily used to store the results of previous planning activ-ity. Although Chapman would object to the use of the word plan, the basic distinction is one that his reply appears to sanction.