Emotional States and Realistic Agent Behaviour

In this paper we discuss some of the relations between cognition and emotion as exemplified by a particular type of agent architecture, the CogAff agent architecture. We outline a strategy for analysing cognitive and emotional states of agents along with the processes they can support, which effectively views cognitive and emotional states as architecture-dependent. We demonstrate this architecture-based research strategy with an example of a simulated multi-agent environment, where agents with different architectures have to compete for survival and show that simple affective states can be surprisingly effective in agent control under certain conditions. We conclude by proposing that such investigations will not only help us improve computer entertainments, but that explorations of alternative architectures in the context of computer games may also lead to important new insights in the attempt to understand natural intelligence and evolutionary trajectories. INTRODUCTION In both artificial intelligence and the design of computer games, the study of emotions is assuming a central role. Building on pioneering early work (Simon 1967; Sloman 1981), it is now widely accepted in the artificial intelligence community that cognition (including intelligence) cannot be understood completely if emotions are left out of the picture. At the same time, the designers of computer games and entertainments have come to realise that emotions or at least mechanisms associated with them are important in the creation of convincing or believable characters. However, to exploit emotion effectively game designers need to understand the differences between purely cosmetic emotional implementations and deeper interactions between cognitive and affective behaviour. In this paper, we outline a strategy for analysing the properties of different agent architectures, the cognitive and affective states and the processes they can support. We illustrate our argument with a scenario demonstrating the surprising effectiveness of simple affective states in agent control, in certain contexts. EMOTIONS AND INTELLIGENCE Minsky (1987) writes in The Society of Mind: “The question is not whether intelligent machines can have emotions, but whether machines can be intelligent without any emotions.” Like many others (e.g. Damasio 1994; Picard 1997) he claims that higher levels of (human-like) intelligence are not achievable without emotions. Unfortunately the concept “emotion” is understood in so many different ways by different people that this is not a well-defined claim. Moreover, some of the evidence purported to establish a link between emotions and higher forms of intelligence shows only that rapid, skillful decisions, rather than analytical deliberations, are sometimes required for intelligence. As argued in (Sloman 1999a) it does not follow that there is any requirement for such episodes to involve emotions, even though emotions are sometimes involved in rapid skillful decisions. The definitional morass can be separated from substantive scientific and technical questions by a strategy which involves exploring a variety of information processing architectures for various sorts of agents. The idea is to use agent architectures to (1) study families of concepts supported by each type of architecture and (2) explore the functional design tradeoffs between different architectures in various contexts. This will help game designers understand the difference between purely cosmetic emotional implementations (e.g. using “emotional” facial expressions or utterances) and deeper interactions between cognitive and affective mechanisms that are characteristic of humans and other animals, where the visible manifestations arise out of processes that are important for the well-being or survival of the individual, or some group to which it belongs. Some of these are relatively simple, e.g. “alarm” mechanisms in simple reactive architectures, which interrupt and override “normal” processing (e.g., being startled by an unexpected noise or movement would be an example of a purely reactive emotion in humans). Other cases are more subtle, e.g. where the use of explicit affective states such as desires or preferences to select behaviours can achieve more flexibility than direct coupling between stimulus and response, for instance allowing both new ways of detecting the presence of needs and new ways of satisfying the same needs in different contexts to be learnt. More sophisticated emotions involving awareness of “what might happen”, (e.g. anxiety) or “what could have happened or could have been avoided” (e.g. regret or shame), require more sophisticated deliberative architectures with extended representational capabilities. Affective states involving evaluation of one’s own internal processes, e.g. the quality of problem solving or the worthiness of desires, need a still more sophisticated reflective architecture with a meta-management layer (Beaudoin 1994). If the operations of that layer can be disrupted by highly “insistent” motives, or memories or concerns, then typically human types of emotional states may emerge out of the ensuing interactions. For instance, infatuation with a member of the opposite sex, embarrassment, excited anticipation, conflicts between desire and duty, can all interfere with attempts to focus on important tasks, because in these states high level processes are interrupted and diverted. These are characteristic of the emotions portrayed in novels and plays. The underlying processes will need to be understood if synthetic characters displaying such emotions in computer entertainments are ever to become as convincing as human actors. Understanding the complex interplay of cognition and emotion in all these different sorts of cases requires close analysis of the properties of different architectures and the states and processes they can support. EXPLORING ARCHITECTURES FOR COGNITION AND AFFECT The “cognition and affect project” at the University of Birmingham is a long term project to study different kinds of architectures and their properties in order to understand the interplay of emotions (and other affective states and processes) and cognition. It addresses questions such as how many different classes of emotions there are, how different sorts of emotions arise (e.g., which ones require specific mechanisms and which ones are emergent properties of interactions between mechanisms with other functions), how emotions fit into agent architectures, how the required architectures can be implemented, what role emotions play in the processing of information, where they are useful, where they are detrimental, and how they affect social interaction and communication. A better understanding of these issues is necessary for a deep and comprehensive survey of types of agents, the architectures they require, their capabilities, and their potential applications (Logan 1998). As part of the project, a particular type of agent architecture, the CogAff architecture (Beaudoin 1994; Wright 1996; Sloman and Logan 1999; Sloman 1998) has been developed, which divides the agent’s cognitive system into three interacting layers (depicted in Figure 1) corresponding to the three types of mechanisms mentioned in the previous section. These are a reactive, a deliberative, and a meta-management layer, all concurrently active, all receiving appropriate sensory input using perceptual mechanisms processing information at different levels of abstraction (as illustrated in Figure 2) and all able to generate action. Each layer serves a particular purpose in the overall architecture, but layers can also influence one another. The reactive layer implements basic behaviours and reflexes that directly control the agent’s effectors and thus the agent’s behaviour, using no mechanisms for representing possible but nonexistent states. This layer can generate chains of internal state-changes as well as external behaviours. In animals it can include chemical mechanisms, analog circuits, neural nets, and condition-action rules. Different sub-mechanisms may all run in parallel performing dedicated tasks. Sometimes they may be activated sequentially. There is no construction of complex descriptions of hypothesised situations or possible plans, though the system may include pre-stored plans whose execution is triggered by internally or externally sensed events. The deliberative layer is a first crucial abstraction over the reactive layer in that it is concerned with the processing of “what-if” hypotheticals involved in planning, predicting or explaining past occurrences. Deliberative mechanisms can vary in complexity and sophistication. A full-fledged deliberative layer will comprise at the very least compositional representational capacities, an associative store of re-usable generalisations, as well as a re-usable working memory for constructing proposed plans, conjectures or explanations, which can be evaluated, compared, adopted, or rejected. The third layer is concerned with self-observation and self-reflection of the agent and provides the possibility for the agent to observe and evaluate aspects of its internal states, and perhaps to control some of them, e.g. by directing attention to specific topics. However, since processes in other layers can sometimes interfere, such control is not total. Meta-management (reflective processes) (newest) Deliberative reasoning ("what if" mechanisms) (older) Reactive mechanisms (oldest) Figure 1: The three layers We conjecture that these three layers represent major transitions in biological evolution. Although most details of the evolutionary trajectories that can produce such multi-layered systems are unknown it is possible that many of the architectural changes will turn out to be examples of the common process of “duplication and divergence” (Maynard Smith and Szathmàry 1999). This model may be contrasted with other kinds of layered models, e.g. where information enters the lowest layer, flows up some abstraction hierarchy, causes decision-making at the top, after which commands flow down via successive expansion processes to the lowest layer which sends signals to motors. The CogAff model also differs from layered hierarchies where higher layers totally dominate lower layers, e.g. some subsumption models. Notice, moreover, that although the functions of the top two layers are different from those of the reactive layer, they will need to be implemented in reactive mechanisms, much as abstract virtual machines in computers are implemented in low level digital mechanisms performing very different operations. Besides clearly distinguishing conceptually different capabilities of agents, and among other advantages, this tripartite division of cognitive systems also provides a useful framework for the study of a wide range of emotions. For example, it turns out that it nicely parallels the division of human emotions into three different classes (Sloman 2000a; Sloman 2000b): • primary emotions (such as “fear” triggered by sensory inputs (e.g. LeDoux 1996) are triggered within reactive mechanisms and influence evolutionarily old responses. • secondary emotions (such as “shame” or “relief” at what did not happen) are triggered in the deliberative layer and may produce a combination of cognitive and physiological processes. • tertiary emotions (such as “adoration” or “humiliation”, where control of attention is reduced or lost) involve the metamanagement layer, though they may be initiated elsewhere.