Emotion as an integrative process between non-symbolic and symbolic systems in intelligent agents

This paper briefly considers the story so far in AI on agent control architectures and the later equivalent debate between symbolic and situated cognition in cognitive science. It argues against the adoption of a reductionist position on symbolically-represented cognition but in favour of an account consistent with embodiment. Emotion is put forward as a possible integrative mechanism via its role in the management of interaction between processes and a number of views of emotion are considered. A sketch of how this interaction might be modelled is discussed. 1. Embodied cognition Within AI, the problem of relating cognition and action has led to a well-known division of opinion since about the mid 1980s between the older symbolic computing position that classically saw action as a one-many decomposition of abstract planned actions from a symbolically-represented control level and the situated agent view, as in Brooks (1986), that saw action as a tight stimulus-response coupling that did not require any symbolic representation. This can be – and originally was – posed as an engineering question of how to produce systems that were able to act competently in the real world, hence the origin of the argument in robotics, where the real world cannot be wished away and where robot sensing systems do not deliver symbols. At this engineering level, the 1990s saw a pragmatic reconciliation of these divergent positions in hybrid architectures, usually with three levels (Gat 97, Arkin and Balch 97, Barnes et al 97), in which the relationship between symbolic planning systems and non-symbolic reactive systems was resolved by giving the reactive systems ultimate control of execution but either allowing planning to be invoked as a resource when needed (for example as a conflict resolution mechanism, or to provide sequencing capabilities) or giving planning systems to ability to constrain and contextualise – but not determine the reactive system in what is sometimes known as supervisory control. However the argument that was carried on from an engineering perspective in robotics, and which to some extent is now a done deal, has subsequently continued at a more scientific level in cognitive science, a discipline within psychology that arguably owed its existence to ideas from classical AI and was heavily influenced by the symbolic world-view as seen in large-scale computational cognitive models such as SOAR (Rosenbloom et al 93) and ACTR (Anderson et al 04). Just as in pre-Brooksian robotics, these models can be criticised for not providing any adequate account of the role of sensing or motor action, which are implicitly seen as peripheral to a cognitive model much as I-O capabilities are peripheral to a computer processor. A deeper criticism arises from a view of agency which sees embodiment as a key starting point rather than an optional extra (Clark 98) and starts from a body in a specific environment that needs a mind to control it rather than a mind considering abstract problems. In the world-view of embodied cognition (Wilson 02), sensori-motor engagement is the ground from which cognitive abilities are constructed (as in Piaget’s developmental psychology); thus neither cognitive abilities nor specific environments and interactions can be detached in the way that had been previously assumed, and a dynamic and process-based view supersedes a declarative and logical-inferencing based view. The Cartesian separation between mind and body which still seems to exist in multi-level architectures is abandoned in favour of brain-body integration, in which processes such as the endocrine system play a vital coordinating role. This does not mean however that a symbolic account of cognition is a pointless activity either from a scientific or an engineering perspective (so we do not actually have to abandon the whole of cognitive science up to now as well as a large chunk of psychology). Just as computational neuro-physiology does not operate at the explanatory level of physics, there is no reason why cognition based on symbolic reference must be reduced to neuro-physiology, even though what is known about the way in which the brain works suggests that symbol manipulation is an emergent property of the dynamic system formed by interaction between neurons. Indeed, the very concept of emergence dictates that an emergent phenomenon is modelled at its own level of representation since it cannot be decomposed into any one of the components whose interaction produces it. Thus an account at the symbolic level may be a valid one as long as it does not incorporate incorrect assumptions about the relationship between this activity and sensori-motor engagement. The power of symbolic reference to abstract out of the current sensory context, to project into imagined contexts, to discretise continuous experiences into conceptual aggregates, to communicate through language and to use the environment to scaffold engagement with the world and other humans (as through writing) has a substantial impact on both cognition and interaction for humans. Thus current work on social agents that aims to produce more human-like systems whether via robots or graphical characters must incorporate symbol-manipulation systems as well as the sensor-driven systems that allow them to act with some degree of competence in their physical or virtual world. However an important characteristic of these human abilities that has not been replicated in computer-based systems is that unification of symbolmanipulation abilities with non-symbolic behaviours driven more directly by sensing that we observe in human activity. In the human case, the hypothesis that symbol manipulation is an emergent property of interaction between brain components suggests that the ability to move smoothly between cognition and reactive engagement with the world is probably a matter of adjusting the interaction between these components and does not therefore require explicit conversion between multiple representations in the way this is typically carried out in current hybrid agent architectures. Unfortunately the computational neuro-physiology account of specific brain subsystems is still fragmentary, and there is no short-term (or even medium-term) likelihood of producing the principled interactional account that this hypothesis requires. Arguably, solving the problem of emergent symbolic reference would not only deal with the origin of language but possibly also with consciousness. It is thus a non-trivial enterprise. How then are we to proceed with an integrated account that is not merely a pragmatic engineering kludge needed to produce competent social agents? The argument of this paper is that one should see process regulation as the key to the enterprise since even if the detailed mechanisms adopted may be invalidated by more extensive models of the brain, the basic approach is likely to be compatible with such models. The specific hypothesis of this paper is that affective systems should be considered as a key component of such regulation because of their role in attentional focus, in relation both to perception and cognition, as well as the management of goals, the allocation of internal resources, and the balance between thinking and acting. 2. Accounts of emotion Just as accounts of action split into two camps in AI from the mid 1980s, there are two corresponding accounts of emotion and its role with respect to agency, one more related to models of the brain and nervous system, and process-oriented, and one related to symbolic models of cognition dealing with goal management and inferencing. The first of these views, which aims at neurophysiologically plausibility, models emotion as part of a homeostatic control mechanism. Often incorporating a model of the endocrine system (Canamero 98) it suggests that emotion should be viewed as the set of brain-body changes resulting from the movement of the current active point of a brain-body process outside of an organism-specific ‘comfort zone’. It does not therefore require a single meterlike component in an agent architecture to represent an emotion, but offers a distributed representation interpretable in terms of the internal process states and external expressive behaviour as an emotion. As well as an independent system state, one can also regard emotion in this framework as modifying the impact of an incoming stimulus. Thus emotion can be incorporated into action selection both indirectly and directly in much the same way as perception, and indeed can be thought of as functioning rather like an internal sensing process concerned with all the other running processes. The second view of emotion is usually known as appraisal theory since it assumes a cognitive appraisal associated with perception that assesses the relationship between symbolic categories established via a model hierarchy using perceptual input and the cognitive-level goals of the agent. Specific appraisal-based theories that have proved highly influential in the construction of graphical characters are those of Ortony et al (1988), which was based on a taxonomy of 22 emotion types, each with an associated appraisal rule, and that of Lazarus (1984), which links appraisal to action via the concept of coping behaviour. Sherer (01) decomposes appraisal into a sequence made up of Relevance Detection, Implication Assessment, Coping Potential Determination and Normative Significance Evaluation, but remains tied to a top-down view of emotion in which cognitive processing results in later physiological changes. Interestingly however one can interpret appraisal as an abstraction on a process of the same type as the first view in which a goal takes the place of a comfort zone. This does not mean that goals could never be independently determined at the cognitive level, but it offers the possibility of propagating the state of the homeostatically-regulated non-symbolic systems into the mo