Analogical reasoning has a long history in artificial intelligence research, primarily because of its promise for Ike acquisition unit effective use of knowledge. Defined as a representational mapping from a known "source " domain into a novel "target" domain, analogy provides a basic mechanism for effectively connecting a reasoner's past and present experience. Using a four-component process model of analogical reasoning, this paper reviews sixteen computational studies of analogy. These studies are organized chronologically within broadly defined task domains of automated deduction, problem solving and planning, natural language comprehension, and machine learning. Drawing on these detailed reviews, a comparative analysis of diverse contributions to basic analogy processes identifies recurrent problems for studies of analogy and common approaches to their solution. The paper concludes by arguing that computational studies of analogy are in a slate of adolescence: looking to more mature research areas in artificial intelligence for robust accounts of basic reasoning processes and drawing upon a long tradition of research in other disciplines.

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Conventional behaviors develop from practice for regularly occurring problems of coordination within a community of actors. Re-using and extending conventional methods for coordinating behavior is the task of everyday reasoning. The

Reuse, system integration, and interoperability create a growing need for capturing, representing, and using application-level information about software-intensive systems and their evolution. In ESPRIT Basic Research Project NATURE, we are developing an integrative approach to requirements management based on a threedimensional framework which addresses formalism as well as cognitive and social aspects. This leads to a new requirements process model which integrates human freedoms through allowing relatively free decisions in given situations. Classes of situations and decisions are defined with respect to the three-dimensional framework through the integration of informal and formal representations, theories of domain modeling, and the explicit consideration of nonfunctional requirements in teamwork. Technical support is provided by a conceptual modeling environment with knowledge acquisition through interactive as well as reverse modeling, and with similarity-based querying. 1

Abstract. It is usually assumed that successful problem solving in knowledge-rich domains depends on the availability of abstract problem-type schemas whose acquisition can be supported by presenting students with worked examples. Conventionally designed worked examples often focus on information that is related to the main components of problemtype schemas, namely on information related to problem-category membership, structural task features, and category-specific solution procedures. However, studying these examples might be cognitively demanding because it requires learners to simultaneously hold active a substantial amount of information in working memory. In our research, we try to reduce intrinsic cognitive load in example-based learning by shifting the level of presenting and explaining solution procedures from a ‘molar ’ view – that focuses on problem categories and their associated overall solution procedures – to a more ‘modular ’ view where complex solutions are broken down into smaller meaningful solution elements that can be conveyed separately. We review findings from five of our own studies that yield evidence for the fact that processing modular examples is associated with a lower degree of intrinsic cognitive load and thus, improves learning.

Typical instances, that is, instances that are representative for a particular situation or concept, play an important role in human knowledge representation and reasoning, in particular in analogical reasoning. This well-known observation has been a motivation for investigations in cognitive psychology which provide a basis for our characterization of typical instances within concept structures and for a new inference rule for justified analogical reasoning with typical instances. In a nutshell this paper suggests to augment the propositional knowledge representation system by a non-propositional part consisting of concept structures which may have directly represented instances as elements. The traditional reasoning system is extended by a rule for justified analogical inference with typical instances using information extracted from both knowledge representation subsystems. Keywords: analogical reasoning, typical instance, hybrid knowledge representation 1 Introduction The traditio...

In adversarial problem solving (APS), one must anticipate, understand and counteract the actions of an opponent. Military strategy, business, and game playing all requfre an agent to construct a model of an opponent that includes the opponent’s model of the agent. The cognitive mechanisms required for such modeling include deduction, analogy, Inductive generalization, and the forma-tion and evaluation of explanatory hypotheses. Explonatory coherence theory captures part of what is involved in APS, particularly In cases involving deception. He who can modvy his tactics in relation to his opponent, and thereby succeed in winning, may be called a heaven-born captain. (Sun Tzu, 1983, p. 29) Many problem-solving tasks involve other people. Often, accomplishment of a task requires coordination with others, an enterprise that might be called cooperative problem solving. Unfortunately, however, we also face problems that require us t6 take into account the actions of opponents; this is adversarial problem solving (APS). Both kinds of social problem solving have been relatively neglected by cognitive science researchers who typically investigate people’s performance on nonsocial problems. This article investigates the cognitive processes required for APS in which one must anticipate and understand the actions of an opponent. A review of several domains of APS-military strategy, business, and game playing-

Metaphor is often seen as a mode of creative thinking or as a means of fostering creativity. However, little work has studied creative generation of novel metaphors. This paper explores the use of computational metaphor identification (CMI) to foster creative generation of novel metaphors. CMI is a technique for analyzing textual corpora to identify potential conceptual metaphors. Drawing those metaphors to readers ’ attention can provide an opportunity to consider alternatives to current metaphors. This paper describes results from a study using CMI to foster metaphorical creativity in the context of science education. The results show that CMI leads to more creative mappings within metaphors. The key contributions of this paper are a demonstration that CMI can be used to foster more original metaphorical reasoning, and, more generally, implications for the study of metaphorical creativity.

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The celebration of the turn of a century is called a “centennial, ” and few