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...

this document. 9.5.2 A comparison of CUE and libido

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.

This paper discusses the design space of believable social robots. We synthesise ideas and concepts from areas as diverse as comics design and rehabilitation robotics. First, we revisit the work of the Japanese researcher Masahiro Mori in the context of recent developments in social robots. Next, we discuss work in the arts into comics design, an area which has dealt for decades with the problem of creating believable characters. Finally, in order to illustrate some of the important issues involved we focus on a particular application area: the use of interactive robots in autism therapy, work that is carried out in the Aurora project. We discuss design issues of social robots in the context of `design spaces' and `niche spaces', concepts that have been defined originally for intelligent agent architectures [26] but which, we propose, can be highly valuable for social robotics design. This paper is meant to open up a discussion towards a systematic exploration of design spaces and niche spaces of social robots.

This paper 1 attempts to characterise a unifying overview of the practice of software engineers, AI designers, developers of evolutionary forms of computation, designers of adaptive systems, etc. The topic overlaps with theoretical biology, developmental psychology and perhaps some aspects of social theory. Just as much of theoretical computer science follows the lead of engineering intuitions and tries to formalise them, there are also some important emerging high level cross disciplinary ideas about natural information processing architectures and evolutionary mechanisms and that can perhaps be unified and formalised in the future. 1

ABSTRACT 1 This paper discusses agent architectures which are describable in terms of the “higher level ” mental concepts applicable to human beings, e.g. “believes”, “desires”, “intends ” and “feels”. We conjecture that such concepts are grounded in a type of information processing architecture, and not simply in observable behaviour nor in Newell’s knowledge-level concepts, nor Dennett’s “intentional stance. ” A strategy for conceptual exploration of architectures in design-space and nichespace is outlined, including an analysis of design tradeoffs. The SIM AGENT toolkit, developed to support such exploration, including hybrid architectures, is described briefly. Keywords:

Agent-based approaches are becoming increasingly important because of their generality, flexibility, modularity, and ability to take advantage of distributed resources. Agents are used in information retrieval, entertainment, coordinating multiple robots, and modeling economic systems. Agents can recommend music, tell stories, and interact with people. They are useful for reducing humans' work and information load in tasks such as medical monitoring and battlefield reasoning. Agents are already changing the way in which we gather information, manage investments, and conduct business. This article provides an introduction to agent issues, outlines motivations for using agent-based paradigms, and describes some of their current uses. Index Terms---Agents, applications, Web agents, monitoring agents, communities, cooperation, protocols, agent decision-making, problem solving. ------------------------------ ###p### ------------------------------ 1INTRODUCTION GENTS have been arou...

Which agent architectures are capable of justifying descriptions in terms of the `higher level' mental concepts applicable to human beings? We propose a new kind of architecturebased semantics for mentalistic descriptions in which mental concepts (e.g. `believes', `desires', `intends', `mood', `emotion ', etc.) are grounded in assumptions about information processing architectures, and not merely in concepts based solely on Dennett's `intentional stance'. These ideas have led to the design of the SIM AGENT toolkit which has been used to explore a variety of such architectures. 1 Architectures and mentalistic descriptions McCarthy [1] gives reasons why we shall need to describe intelligent robots in mentalistic terms, and why such a robot will need some degree of self consciousness. He also proposes a notation that we and the robot might use to describe its states. We extend that work by focusing on the underlying `high level' architectures required to justify ascriptions of mentality...

The common view that the notion of a Turing machine is directly relevant to AI is criticised. It is argued that computers are the result of a convergence of two strands of development with a long history: development of machines for automating various physical processes and machines for performing abstract operations on abstract entities, e.g. doing numerical calculations. Various aspects of these developments are analysed, along with their relevance to AI, and the similarities between computers viewed in this way and animal brains. This comparison depends on a number of distinctions: between energy requirements and information requirements of machines, between ballistic and online control, between internal and external operations, and between various kinds of autonomy and self-awareness. The ideas are all intuitively familiar to software engineers, though rarely made fully explicit. Most of this has nothing to do with Turing machines or most of the mathematical theory of computation. But it has everything to do with both the scientific task of understanding, modelling or replicating human or animal intelligence and the engineering applications of AI, as well as other applications of computers. 1