robo-CAMAL: A BDI Motivational Robot

Abstract

Motivation is a central concept in the development of autonomous agents and robots. This paper describes an architecture that uses a psychological BDI model of reasoning, combined with a distributed multi-level model of motivation. The robot controlling architecture makes use of a generic set of deliberative components plus an environment task-centred set of reactive components that reflect the architecture's embodiment. The architecture has been used in a number of simulated environments and here is used to control a mobile robot. A theoretical framework for motivation and affect is given, and related to the nature of autonomy and embodiment. A BDI model, based on a psychological model of reasoning in a 5 year old child, is described in terms of the nature of motivation and affect within the architecture. Finally, criteria for judging the nature of an agent's motivation are introduced, and used to validate the motivational constructs implemented within the architecture. Experimental results lead to a comparative discussion.

Reasoning with BDI Robots: From simulation to physical environment – Implementations and Limitations

“…It is essentially a design that addresses the question of what is a Theory of Cognition, as described by Newell [50], and tries to answer some of the issues that comprise Norman's Cognitive Science agenda [30]. Based on earlier work [51,52], it has been developed to address the limitations of initial and incremental implementations [15,16,53,54]. While the CAMAL project draws on research in human cognition, it addresses many of the issues raised above and in doing so aims to produce adaptable machine intelligence, and not to produce human-like artificial intelligence.…”

“…It was developed initially with a view to being implemented on single agents in mainly simulation work with limited application to the control of robots in a physical environment. CAMAL, and its variants was moderately successful in the control of single robots [15,16]. Here we consider its limitations and the path forward to its use in the control of multiple robots of differing capabilities.…”

“…The roboCAMAL sub-project reported in [15] made use of reinforcement learning to modify task models; current implementations lack this feature, although it is part of the sub-project described in section 6. The task model used can vary over the lifetime of a CAMAL experiment as a result of goal and intention evaluation.…”

“…Motivator evaluation modifies the values associated with Goals and Associations based on the success or failure of the motivator, and the current Norms controlling the BDI. The adaption parameter, as explored in the experiments presented in [15], is a real valued number in the range [0, 1].…”

“…The architectures developed here do this through the utilisation of metrics across control states that are realised in a probabilistic Belief-Desire-Intention reasoning model. Earlier research on cognitive architectures has focused on the control of a single agent, using either simulations [14] or a single robot [15,16] in known and dynamic environments (typically populated by non-collaborating agents or robots). Here reasoning and representational issues that needed to be addressed, in moving from simulation and single robot work towards colonies of interacting robots in unknown environments, are outlined and discussed.…”