Computation for Metaphors, Analogy, and Agents

As an introduction to papers in this book we review the notion of metaphor in language, and of metaphor as conceptual, and as primary to understanding. Yet the view of metaphor here is more general. We propose a constructive view of metaphor as mapping or synthesis of meaning between domains, which need not be conceptual ones. These considerations have implications for artificial intelligence (AI), humancomputer interaction (HCI), algebraic structure-preservation, constructive biology, and agent design. In this larger setting for metaphor, contributions of the selected papers are overviewed and key aspects of computation for metaphors, analogy and agents highlighted. 1 Metaphor beyond Language and Concepts Metaphor and analogy had traditionally been considered the strict domain of rhetoric, poetics and linguistics. Their study goes back in long scholarly histories at least to the ancient Greece of Aristotle and the India of Panini. More recently it has been realized that human metaphor in language is primarily conceptual, and moreover that metaphor transcends language, going much deeper into the roots of human concepts, epistemologies, and cultures. Seen as a major component in human thought, metaphor has come to be understood and studied as belonging also to the realm of the cognitive sciences. Lakoff and Johnson’s and Ortony’s landmark volumes [22,36] cast metaphor in cognitive terms (for humans with their particular type of embodiment) and shed much light on the constructive nature of metaphorical understanding and creation of conceptual worlds. Our thesis is that these ideas on metaphor have a power extending beyond the human realm, not only beyond language and into human cognition, but to the realm of animals, as well as robots and other constructed agents. In building robots and agents, we are engaging in a kind of constructive biology, working to realize the mechanism-as-creature metaphor, which has guided and inspired much work on robots and agents. Such agents may have to deal with aspects of Current address: Interactive Systems Engineering, Department of Computer Science, University of Hertfordshire, Hatfield, Hertfordshire AL10 9AB, United Kingdom, Email: c.l.nehaniv@herts.ac.uk C. Nehaniv (Ed.): Computation for Metaphors, Analogy, and Agents, LNCS 1562, pp. 1–11, 1999. c © Springer-Verlag Berlin Heidelberg 1999 2 Chrystopher L. Nehaniv time, space, mapping, history and adaptation to their respective Umwelt (“world around”). By looking at the linguistic and cognitive understanding of metaphor and analogy, and at formal and computation instantiations of our understanding of metaphor and analogy, the constructors of agents, robots and creatures may have much to gain. Understanding through building is a powerful way to validate theories and uncover explanatory mechanisms. Moreover, building can open one’s eyes to the light of new understanding in both theory and practice. An intriguing metaphorical blend is the notion of a Robot. The concept of a robot is understood as a cognitive blend of the concepts “machine” and “human” (or “animal”). Attempting to build such a mechanism, one is led to the question of ‘transferring’ – realizing analogues of – human or animal-like abilities in a new medium. Moreover, if this new mechanism should act like an animal, How will need to interact with, adapt to, and perhaps interpret the world around it? How is this agent to ‘compute’ in this way? and, How is it to be engineered in order to meet either or both of these mutually reflective goals? Scientific advances (and delays) have often rested on metaphors and analogies, and paradigmatic shifts may be largely based on them [20]. But computation employing conceptual metaphors has mostly been carried out via human thought. In the realms of human-computer interaction (HCI), artificial intelligence (AI), artificial life, agent technology, constructive biology, cognitive science, linguistics, robotics, and computer science, we may ask for means to employ the powerful tool of metaphor for the synthesis and analysis of systems for which meaning makes sense, for which a correspondence exists between inside and outside, among behaviors, embodiments and environments. Richards [37] formulated a metaphor as a mapping from a topic source domain (‘tenor’) to a target domain (‘vehicle’), by means of which something is as1 The machine itself as tool and metaphor has had a long and creative history [11]. Indeed the conceptualization of what we consider mechanistic explanations in physics, biology and engineering has changed very much in course of the history of ideas. For instance, Newton’s physics was criticized as being non-mechanistic, since it required action at a distance without interconnecting parts (Toumlin [43]). Modern mechanistic scientific explanations were not necessary mechanistic in the older sense of the term. ‘Mechanistic’ represents a refined, blended concept that has evolved over many centuries. 2 A related blend is the notion of ‘cyborg’ — a ‘cybernetic organism’, which is more proximal for us than ‘robot’ in that it entails a physical blending of biological life, including ourselves, with the machine. Indeed, our use of tools such as eyeglasses, hammers, numerical notations, contact lenses, and other prosthetics to augment our bodies and minds has already made us cyborgs. This can be taken as an empowering metaphor when one takes control of and responsibility for our own cybernetic augmentation (Haraway [12], Nehaniv [28]). 3 See the discussion paper “The Second Person — Meaning and Metaphors” [30] at the end of this book for outline of a theory of meaning in a setting extending Shannon-Weaver information theory to situated agents and observers and addressing the origin, evolution and maintenance of interaction channels for perception and action. Computation for Metaphors, Analogy and Agents 3 serted (or understood) about the topic. Cognitive theories realized that metaphor is not an exceptional decorative occurrence in language, but is a main mechanism by which humans understand abstract concepts and carry out abstract reasoning (e.g. Lakoff and Johnson [22], Lakoff [21], Johnson [18]). On this view, metaphors are structure-preserving mappings (partial homomorphisms) between conceptual domains, rather than linguistic constructions. Common metaphorical schemas in our cultures are grounded in embodied perception. Correspondences in experience (rather than just abstract similarity) structure our cognition. Common conceptual root metaphors in English are studied by Lakoff and Johnson [22], as also extended with detailed attention to root analogies in the English lexicon by Goatly [9]. An important extension for conceptual metaphors is the framework of Mark Turner and Gilles Fauconnier (see Turner’s paper in this volume), who argue that metaphors and analogies are not sufficiently accounted for by mappings between pre-existing static domains, but are actually better understood as constructs in forged conceptual spaces, which are blends of conceptual domains, over some common space, with projections from the blend space back to the constituent factors (e.g. a ‘tenor’ and ‘vehicle’) affording recruitment of features from the blend space in which much inference and new structure may be generated. We shall not restrict ourselves to concepts or language. A more general, not necessarily symbolic view is also possible if one conceives of metaphor and analogy in the study of ‘meaning transfer’ between domains, or in light of the theory of cognitive blending, as the realm of ‘meaning synthesis’ by putting things together that already share something to create a new domain guiding thought, perception or action. Other types of meaning can be seen for instance in Dawkins’ notion of memes as replicators in minds and cultures [7], transmitted by imitation and learning, propagating, often in difficult circumstances, via motion through behavioral or linguistic media. Still another type of meaning is comprised by agent behavior in response to sensory stimuli to effect changes in its environment. 1.1 Human-Computer Interaction The idea of metaphor has been applied in Human-Computer Interaction (HCI), Cognitive Ergonomics, and Software Engineering. For example, building user interfaces based on metaphors is now standard engineering practice. Examples are windows, widgets, menus, desktops, synthetic worlds (e.g. nanomolecular manipulation via a virtual reality (VR) and force-feedback haptic interface), and personal assistant agents). The search for improved interaction metaphors is an active research and development area (e.g. [41]). Here we are in a realm of metaphor in human-tool interaction, which is clearly primarily conceptual (and at times merely sensorimotorial) rather than linguistic. Language games have 4 The understanding of readers with an knowledge of basic category theory may be enhanced by the suggestion that Fauconnier-Turner blends may be considered as category-theoretic pushouts or, more generally, as colimits of conceptual domains. 4 Chrystopher L. Nehaniv become interaction games, with the meaning of artifacts defined by the actions they afford. A particular case is the area of ‘intelligent software agents’. This has grown into a large arena of research and application, concerned with realizing the software-as-agent metaphor in interfaces, entertainment and synthetic worlds, as well as for workload and information overload reduction (cf. [38]). As with other types of semantic change in human language and cultures, what may at first have been marked as strange may become common: these metaphors become definitional identities; rather than conceptual mappings, they become realities. Some pieces of software are really agents. 1.2 Algebraic Engineering: Preserving Structure Can the creativity of human metaphor be understood in formal terms? How do humans understand each other’s metaphors and analogies? What if the humans liv