Modelling Motivation and Action Control in Cognitive Systems

Abstract

The traditional way to define – and model – cognition, from the mid-fifties onward, has been to focus on deliberation, i.e., on those inferential processes that operate on well-defined symbolic mental representations in order to get a task accomplished that would require intelligence for human beings to solve. Consequently, AI programs, as well as computer models of psychological processes, were largely confined to a world of symbols. Only a few projects attempted to overcome these limitations and take a step towards more realistic interaction, such as Winograd’s famous SHRDLU (Winograd, 1972). Still, the seminal work accomplished in GPS (Newell & Simon, 1963) and STRIPS (Fikes & Nilsson, 1971) continues to be the anchor point for most of AI and cognitive science alike. Recent years, however, have brought a veritable paradigm shift: interaction with the ‘real’ environment – physical, or human users, or other ‘agents ’ – has been brought to the fore; and ‘situatedness ’ (Suchman, 1987) and the ability for communication and co-operation (as in distributed AI) have become important criteria. The basic nature of biological cognitive systems, including humans, has been recognise