Social planning for social HRI

Making a computational agent 'social' has implications for how it perceives itself and the environment in which it is situated, including the ability to recognise the behaviours of others at various levels – simple actions, goals and intentions. Hence fundamental elements of an architecture for social agents must allow for management of social motivations-i.e. to reach social goals, not only practical goals-and must model and account for actions having both practical and social effects. Further it has been argued that to build social agents it is not sufficient to just add a few 'social modules' to existing architectures: while multilayer computational cognitive models have been studied for some time, c.f. [1], a new layered deliberation architecture is required that at the higher level(s) naturally accommodates analysis of decision choices that take into account both rich context and future projections of possible consequences, yet does not rely on computational expensive deep reasoning capability [2], [3], [4]. In the work reported here, we do not attempt to address the 'large' questions associated with the design of a fully integrated computational cognitive architecture; rather we adopt a relatively narrow focus on exploiting and extending epistemic planning mechanisms to achieve run-time generation of plans in rich multi-actor contexts, i.e. we seek to construct social plans in settings where the social context is relevant in the assessment of the beliefs and capabilities of others, and in making appropriate choices of what to do next. Our approach has been informed by our experience with the BDI model of agency [5] and several associated agent architectures-architectures that were introduced to support a balance of deliberative and reactive behaviours, and that in their instantiation are reliant on domain-specific expert knowledge acquisition to provide a knowledge level view [6], c.f. [7], [8]. We are also supporters of the position that logic-based techniques are well suited to represent social reasoning and through which to engineer effective mechanisms, c.f. [9], [10], [11]. Fundamental concepts we build on include: reasoning about the beliefs of others, including their beliefs about others; establishing common ground; and the use of stereotypes. So a few words about each. Exploiting mutual awareness to enable a participant engaged in collaborative activity with others to select an appropriate action typically involves Theory of Mind (ToM) reasoning [12], [13], i.e., reasoning about the knowledge, beliefs, perspectives and reasoning of other participants. Agent-based computational models have been used to investigate higher-order …