We present and evaluate an approach to the reasoning needed to handle a broad class of metaphorical ut-terances, and a computer program (ATT-Meta) partially implementing and further specifying that approach. The approach emanates from artificial intelligence but is offered also for consideration by cognitive scien-tists generally. The utterances of interest are ones that (a) rest on conceptual metaphors that are familiar to the understander but (b) transcend the mappings in the conceptual metaphors by using concepts not han-dled by the mappings. Our approach advocates possibly-extensive inferencing in the terms of the source (vehicle) domains of the conceptual metaphors, while avoiding as far as possible the extension of the map-pings to deal with the concepts they do not handle. The general approach is similar in flavor to those of a small number of other metaphor researchers, but we provide a more extensive analysis, additional principles and a more thorough-going implementation. The approach contains a number of “view-neutral mapping adjuncts, ” which are default mapping principles that enable important source-domain aspects to be mapped to the target domain, independently of which specific metaphorical views are in play. Many discussions of metaphor appear to assume that such mapping actions occur, but rarely address them systematically and explicitly. In addition, in the approach, a conceptual metaphor can consist not only of a between-domain mapping but also of special, ancillary assumptions that serve to enrich the source domain with specific de-tails needed by the metaphors. The implemented system supports ancillary assumptions but currently only has a preliminary handling of view-neutral mapping adjuncts. 2 1

There exist a broad class of metaphorical utterances that, although widely reported, have received little detailed examination. These metaphors are not totally novel; instead, the vehicles contain novel extensions to familiar conceptual metaphors. That is, they (a) rest on a conceptual metaphor that is familiar to the understander but (b) transcend the list of mappings in the conceptual metaphor because they contain concepts not handled by them. We argue against the creation of new source to target mappings for these novel concepts and argue instead that (possibly extensive) reasoning can occur in the source domain. This allows these concepts to connect either to the mappings in an existing conceptual metaphor, or to mappings which are adjuncts to any conceptual metaphor. We justify these adjunct mappings and provide a partial inventory of their types. We believe our approach has benefited from the discipline imposed by implementing it in a computer system. 1

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Abstract. We present an approach and an associated computer program (ATT-Meta) which can interpret novel uses of familiar metaphors. ATT-Meta performs sophisticated reasoning with uncertainty of a kind that is required for reasoning about the content of metaphorical utterances. ATT-Meta’s handling of uncertainty is qualitative and handles potential conflicts between different lines of reasoning or arguments. It also uses a particular approach to specificity, which involves a complex examination of the complete argument structures supporting the conflicting hypotheses/arguments, as a powerful tool for comparing arguments. 1

Why is Artificial Intelligence concerned with metaphor, and what special contributions can AI offer to metaphor research? This chapter will indicate why AI needs to study metaphor and will outline what AI has been contributing to the illumination of metaphor, whether it is processed by artefacts or by the human mind.

We illustrate how the use of metaphorical views for reasoning with metaphor requires the mapping of information such as event shape, event rate and men- tal/emotional states from the source do- main to the tat'get domain. Such mappings are domain-independent and can be implemented by means of rules we call View Neutral Mapping Adjuncts (VNMAs). We give a list of the main VNMAs that appear to be required, and show how they can be incorporated into a pre-existing system (ATT-Meta' for metaphorical reasoning.

Metaphor involves the use of a source domain (vehicle domain) to affect a target domain (tenor domain) in some way. For instance, the source domain may be used to generate hypotheses about the target domain or to highlight parts of the target domain. Typically these effects can be cast as the transfer of pieces of information or other items from the source domain to the target domain. Such transfer is extensively addressed in the literature. However, transfer in the reverse direction is rarely discussed in any detail, and then only for limited purposes. This article argues that "reverse" transfer from target to source is of much more widespread, fundamental importance than seems to have been realized. One of the conclusions is that it is often more effective, during discourse understanding, to translate information from literal sentences into prevailing metaphorical terms than to translate the information from metaphorical sentences into literal terms. The issues have been obscured by failure to distinguish between different notions of direction in metaphor: intuitive directions of static source/target mappings; directions of individual transfers; and direction of main intended transfer. In particular, clarifying these different notions of direction throws new light on what the asymmetry of metaphor amounts to. The article also briefly explains the support for target-to-source transfer in an AI program for metaphorical reasoning called ATT-Meta, implemented in an AI project. However, the issues discussed are relevant to all disciplines concerned with metaphor.

A detailed approach has been developed for core aspects of the task of understanding a broad class of metaphorical utterances. The utterances in question are those that depend on known metaphorical mappings but that nevertheless contain elements not mapped by those mappings. A reasoning system has been implemented that partially instantiates the theoretical approach. The system, called ATT-Meta, will be demonstrated. The paper briefly indicates how the system works, and outlines some specific aspects of the system, approach and the overall project.

We consider the varieties and directions of influence that the source and target domains involved in a conceptual metaphor can have on each other during the course of understanding metaphorical utterances based on the metaphor. Previous studies have been restricted both as to direction of influence and as to type of influence. They have been largely confined to the "forward" (source to target) direction of influence, and they have concentrated on the transfer of features or propositions and (to some extent) the highlighting of aspects of a domain. By contrast, this article stresses the importance both of other varieties of influence (e.g., transfer of queries and uncertainty effects) and of "reverse" influence (target to source). We seek to curb the natural tendency to think that, because metaphor involves an overall move from source to target, therefore all inter-domain influences in the course of understanding go in that direction. The bulk of the article explores the theoretical issues involved. These issues have arisen out of a reasoning-based approach to metaphor, seeking to make best use of information from metaphorical utterance, context, source and target domains, and known metaphorical mappings, by applying a complex overall reasoning process. The article briefly explains the thorough implementation of reverse influence in a computer program for metaphorical reasoning called ATT-Meta, although the theoretical considerations are relevant to all disciplines concerned with metaphor. We make some suggestions for further computational and psychological research on metaphor.