This article describes an integrated theory of analogical access and mapping, instantiated in a

The authors present a theory of how relational inference and generalization can be accomplished within a cognitive architecture that is psychologically and neurally realistic. Their proposal is a form of symbolic connectionism: a connectionist system based on distributed representations of concept meanings, using temporal synchrony to bind fillers and roles into relational structures. The authors present a specific instantiation of their theory in the form of a computer simulation model, Learning and Inference with Schemas and Analogies (LISA). By using a kind of self-supervised learning, LISA can make specific inferences and form new relational generalizations and can hence acquire new schemas by induction from examples. The authors demonstrate the sufficiency of the model by using it to simulate a body of empirical phenomena concerning analogical inference and relational generalization. A fundamental aspect of human intelligence is the ability to form and manipulate relational representations. Examples of relational thinking include the ability to appreciate analogies between seemingly different objects or events (Gentner, 1983; Holyoak & Thagard, 1995), the ability to apply abstract rules in novel situations (e.g., Smith, Langston, & Nisbett, 1992), the ability to understand and learn language (e.g., Kim, Pinker, Prince, & Prasada, 1991), and even the ability to appreciate perceptual similarities

. 1 A hybrid (symbolic/connectionist) cognitive architecture, DUAL, is proposed. It is a multi-agent system which consist of a large number of non-cognitive, relatively simple agents, and which behaviour emerges from the behaviour of these simple agents and the interactions between them. The agents within this architecture have no internal knowledge base and goals. They are both computational devices and representational elements. They have internal (local) memory and hardwired processes that they can run. DUAL is hybrid at the micro level (i.e. it consists of hybrid agents) rather than at the macro level (i.e. it does not consist of separate symbolic and connectionist modules). From the symbolic perspective each agent represents a piece of world knowledge and performs some specific task while from the connectionist perspective it computes simply the activation level of the agent which reflects its relevance to the context. In this way symbolism and connectionism are considered as d...

This paper presents several challenges to the models of analogy-making, namely the need for building integrated models, the need for using dynamic and emergent representations, the need for using dynamic and emergent computation, and the need to integrate analogy-making with other cognitive processes. Some experimental data are reviewed which substantiate these needs and the main ideas how the AMBR model of analogy-making could meet these challenges are presented. 1. From the Anatomy towards the Physiology of AnalogyMaking: The Need for Integrated and Dynamic Models For a long time now the research on analogy has concentrated on the anatomy of analogymaking, i.e. on decomposing it into pieces (representation building, retrieval, mapping, transfer, evaluation, learning) and trying to understand how each individual piece works. A number of successful models of various subprocesses (mainly of mapping and retrieval) have been built which account for most of the psy...

Models that rely exclusively on static representations cannot account fully for the flexibility of human analogy-making. More

We know that misinformation presented in interrogating questions or in advertising produces blendings, that even imagining a possible episode might produce blending as well, however, we do not know whether reasoning and problem solving can produce the same effect. On the other hand, models of analogy-making assume "perfect memory" for old episodes. The AMBR model of analogical problem solving has mechanisms for interaction between memory and reasoning which explain partial memory and memory distortions and has predicted blending effects which are due to the reasoning process. Such predictions have no parallel in any other model we know of. There has been no experimental support for these predictions so far. The current paper describes an experiment explicitly designed to test these predictions. It consists of three sessions: 1) solving three problems, 2) solving two more target problems by analogy with some of the problems in the first session, and 3) reproduction of the three problems in the first session. The results demonstrate that the degree of blending in the recalled stories depends on the target problem solved in the second session.

This is Appendix A to the thesis " Learning to See Analogies: a Connectionist Exploration."

This paper presents an unusual prediction made by the DUAL-based model of judgment JUDGEMAP and its verification. The model is shortly presented as well as the simulation data obtained with it. These data predict that people will use the information on an irrelevant dimension when judging another dimension. This prediction is then tested in a psychological experiment and confirmed.

This paper presents a series of simulation experiments related to the interaction of memory and analogy-making in the AMBR model. This interaction makes it possible to demonstrate blending between superficially dissimilar episodes as a result of the established analogical mapping between them and of superficially and structurally dissimilar episodes as a result of a double analogy with a third episode. Both simulation experiments model the blending effect of analogy-making. The conditions for the emergence of such blending are explored on the basis of a proposed specific analogy-like retrieval mechanism.

Abstract. A new approach to building robots with anticipatory behavior is presented. This approach is based on analogy with a single episode from the past experience of the robot. The AMBR model of analogy-making is used as a basis, but it is extended with new agent-types and new mechanisms that allow anticipation related to analogical transfer. The role of selective attention on retrieval of memory episodes is tested in a series of simulations and demonstrates the context sensitivity of the AMBR model. The results of the simulations clearly demonstrated that endowing robots with analogy-based anticipatory behavior is promising and deserves further investigation. 1