There is much shallow thinking about emotions, and a huge diversity of definitions of “emotion ” arises out of this shallowness. Too often the definitions and theories are inspired either by a mixture of introspection and selective common sense, or by a misdirected neo-behaviourist methodology, attempting to define emotions and other mental states in terms of observables. One way to avoid such shallowness, and perhaps eventually achieve convergence, is to base concepts and theories on an information processing architecture, which is subject to various constraints, including evolvability, implementability, coping with resource-limited physical mechanisms, and human-like functionality. Within such an architecture-based theory we can distinguish (at least) primary emotions, secondary emotions, and tertiary emotions, and produce a coherent theory which explains a wide range of phenomena and also partly explains the diversity of theories: most theorists focus on only a subset of types of emotions.

A problem which bedevils the study of emotions, and the study of consciousness, is that we assume a shared understanding of many everyday concepts, such as `emotion', `feeling', `pleasure', `pain', `desire', `awareness', etc. Unfortunately, these concepts are inherently very complex, ill-defined, and used with different meanings by different people. Moreover this goes unnoticed, so that people think they understand what they are referring to even when their understanding is very unclear. Consequently there is much discussion that is inherently vague, often at cross-purposes, and with apparent disagreements that arise out of people unwittingly talking about different things. We need a framework which explains how there can be all the diverse phenomena that different people refer to when they talk about emotions and other affective states and processes. The conjecture on which this paper is based is that adult humans have a type of information-processing architecture, with components whi...

There are evolutionary trajectories in two different but related spaces, design space and niche space. Co-evolution occurs in parallel trajectories in both spaces, with complex feedback loops linking them. As the design of one species evolves, that changes the niche for others and vice versa. In general there will never be a unique answer to the question: does this change lead to higher fitness? Rather there will be tradeoffs: the new variant is better in some respects and worse in others. Where large numbers of mutually interdependent species (designs) are co-evolving, understanding the dynamics can be very difficult. If intelligent organisms manipulate some of the mechanisms, e.g. by mate selection or by breeding other animals or their own kind, the situation gets even more complicated. It may be possible to show how some aspects of the evolution of human minds are explained by all these mechanisms.

In this paper we present a toolkit for implementing architectures for intelligent robotic systems. This toolkit is based on a previously developed architecture schema (a set of architecture design rules). The purpose of both the schema and toolkit is to facilitate research into information-processing architectures for state-of-the-art intelligent robots, whilst providing engineering solutions for the development of such systems. A robotic system implemented using the toolkit is presented to demonstrate its key features. 1

This thesis presents an approach to generating intelligent behaviour for agents in computer game-like worlds. Designing and implementing such agents is a difficult task because they are required to act in real-time and respond immediately to unpredictable changes in their environment. Such requirements have traditionally caused problems for AI techniques. To enable agents to generate intelligent behaviour in real-time, complex worlds, research has been carried out into two areas of agent construction. The first of these areas is the method used by the agent to plan future behaviour. To allow an agent to make efficient use of its processing time, a planner is presented that behaves as an anytime algorithm. This anytime planner is a hierarchical task network planner which allows a planning agent to interrupt its planning process at any time and trade-off planning time against plan quality. The second area of agent construction that has been researched is the design of agent architectures. This has resulted in an agent architecture with the functionality to support an anytime planner in a dynamic, complex world. A proof-of-concept implementation of this design is presented which plays Unreal Tournament and displays behaviour that varies intelligently as it is placed under pressure.

Here we describe an architecture for an autonomous software agent designed to model a broad spectrum of human cognitive and affective functioning. In addition to featuring “consciousness ” the architecture accommodates perception, several forms of memory, emotions, action-selection, deliberation, ersatz language generation, several form of learning, and metacognition. One such software agent, IDA, embodying much of this architecture, is up and running. IDA’s “consciousness” module is based on global workspace theory, allowing it to select relevant resources with which to deal flexibly with both exogenous and endogenous stimuli. Within this architecture emotions implement IDA’s drives, her 1 primary motivations. Offering one possible architecture for a fully functioning artificial mind, IDA constitutes an early attempt at the exploration of design space and niche space. The design of the IDA architecture spawns hypotheses concerning human cognition and affect that can serve to guide the research of cognitive scientists and neuroscientists. One such hypothesis is that consciousness is discrete.

This paper complements McCarthy’s “The well designed child”, in part by putting it in a broader context, the space of possible well designed progeny, and in part by relating design features to development of mathematical competence. I first moved into AI in an attempt to understand myself, especially hoping to understand how I could do mathematics. Over the ensuing four decades, my interactions with AI and other disciplines led to: design-based, cross-disciplinary investigations of requirements, especially those arising from interactions with a complex environment; a draft partial ontology for describing spaces of possible architectures, especially virtual machine architectures, for behaving systems (including our precursors); investigations of varied forms of representation and how they are suited to different functions; analysis of biological nature/nurture tradeoffs and their relevance to future machines; studies of control issues in a complex architecture; and showing how the states and processes possible in such an architecture relate to our (simplified) intuitive concepts of motivation, feeling, preferences, emotions, attitudes, values, moods, consciousness, etc. In 1971 I thought working models of human vision could lead to models of visual/spatial reasoning that would help to support Kant’s view of mathematics, against Hume’s. This has not yet happened, but I am still exploring

The CoSy Architecture Schema Toolkit (CAST) is a new software toolkit, and related processing paradigm, which supports the construction and exploration of information-processing architectures for intelligent systems such as robots. CAST eschews the standard point-to-point connectivity of traditional message-based software toolkits for robots, instead supporting the parallel refinement of representations on shared working memories. In this article we focus on the engineering-related aspects of CAST, including the challenges that had to be overcome in its creation, and how it allow us to design and build novel intelligent systems in flexible ways. We support our arguments with example drawn from recent engineering efforts dedicated to building two intelligent systems with similar architectures: the PlayMate system for tabletop manipulation and the Explorer system for human-augmented mapping. Key words: intelligent robotics, artificial intelligence, information-processing architectures, middleware 1.

This paper presents a developing concept of mind defined in terms of external and internal niches. This perspective on mind is described primarily in terms of the niche space of control states and the design space of processes that may support such phenomena. A developing agent architecture, that can support motivation and other control states associated with mind, is presented. Different aspects of agent research are discussed in terms of three categories of agents. Each agent category is characterized primarily in terms of their task-related competencies and internal behaviors and discussed in terms of our taxonomy of control states. The concept of complete agents is then introduced. Goals are described in terms of their generation across a number of computational layers. Experimental analysis is provided on how these differing forms of behaviors can be cleanly integrated. This leads into a discussion on the nature of motivational states and the mechanisms used for making decisions and managing the sometimes-competitive nature of processes internal to a complex agent. The difficulty of evaluating complete agents is discussed from a number of perspectives. The paper concludes by considering future directions related to the computational modeling of emotions and the concept of synthetic mind. Keywords: Agent architectures, synthetic mind, control states, design space, niche space.

The rich and exciting research over the past decade concerning the description, analysis, controller design, simulation, and implementation of distributed systems is reviewed. From control engineering, this research has inherited the concepts and theories of optimality, stability, controlled differential equation models, and the motivation to improve the performance of increasingly complex physical processes. From computer science, the research has incorporated the theories of logical specification and verification, event-driven state machine models, concurrent processes and object-oriented approaches. The review is organized in the framework of dynamic networks of hybrid automata (DNHA). The case study of an automated highway system (AHS) is used to illustrate the challenges posed by a complex distributed system, and the research contributions that address different challenges. There is an equal emphasis on the conceptual and theoretical contributions and on tools and techniques that yield more immediately practical benefits.