This thesis defines a set of program restructuring operations (refactorings) that support the design, evolution and reuse of object-oriented application frameworks. The focus of the thesis is on automating the refactorings in a way that preserves the behavior of a program. The refactorings are defined to be behavior preserving, provided that their preconditions are met. Most of the refactorings are simple to implement and it is almost trivial to show that they are behavior preserving. However, for a few refactorings, one or more of their preconditions are in general undecidable. Fortunately, for some cases it can be determined whether these refactorings can be applied safely. Three of the most complex refactorings are defined in detail: generalizing the inheritance hierarchy, specializing the inheritance hierarchy and using aggregations to model the relationships among classes. These operations are decomposed into more primitive parts, and the power of these operations is discussed from the perspectives of automatability and usefulness in supporting design. Two design constraints needed in refactoring are class invariants and exclusive components. These constraints are needed to ensure that behavior is preserved across some refactorings. This thesis gives some conservative algorithms for determining whether a program satisfies these constraints, and describes how to use this design information to refactor a program.

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.

High-level perception—the process of making sense of complex data at an abstract, conceptual level—is fundamental to human cognition. Through high-level perception, chaotic environmen-tal stimuli are organized into the mental representations that are used throughout cognitive pro-cessing. Much work in traditional artificial intelligence has ignored the process of high-level perception, by starting with hand-coded representations. In this paper, we argue that this dis-missal of perceptual processes leads to distorted models of human cognition. We examine some existing artificial-intelligence models—notably BACON, a model of scientific discovery, and the Structure-Mapping Engine, a model of analogical thought—and argue that these are flawed pre-cisely because they downplay the role of high-level perception. Further, we argue that perceptu-al processes cannot be separated from other cognitive processes even in principle, and therefore that traditional artificial-intelligence models cannot be defended by supposing the existence of a “representation module ” that supplies representations ready-made. Finally, we describe a model of high-level perception and analogical thought in which perceptual processing is integrated with analogical mapping, leading to the flexible build-up of representations appropriate to a given context. 1 The Problem of Perception One of the deepest problems in cognitive science is that of understanding how people make sense of the vast amount of raw data constantly bombarding them from their environment. The essence of human perception lies in the ability of the mind to hew order from this chaos, whether this means simply detecting movement in the visual field, recognizing sadness in a tone of voice, perceiving a threat on a chessboard, or coming to understand the Iran–Contra affair in terms of

Analogy is often viewed as a partial similarity match between domains. But not all partial similarities qualify as analogy: There must be some selection of which commonalities count. Three experiments tested o particular selection constraint in anological mapping, namely, systemoticity. That is, we tested whether a given predicate is more likely to figure in the interpretation of and prediction from on analogy if the predicate participates in a common system of relations. In Experiment 1, subjects judged two matches to be included in on analogy: an isolated match, and a match embedded in. a larger matching system. Subjects preferred the embedded match. In Experiments 2 and 3, subjects mode analogical predictions about a target domain. Subjects predicted information that followed from a causal system that matched the base domain, rather than information that was equally plausible, but that created an isolated match with the base. Results support Gentner's (1983, 1989) structure. mopping theory in that anological mopping concerns systems and not individual predicates, and that attention to shored systematic structure constrains the selection of information to include in an analogy.

Similarity-based and rule-based accounts of cognition are often portrayed as opposing accounts. In this paper we suggest that in learning and development, the process of comparison can act as a bridge between similarity-based and rule-based processing. We suggest that comparison involves a process of structural alignment and mapping between two representations. This kind

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Our ability to see a particular object or situation in one context as being “the same as” another object or situation in another context is the essence of analogy-making. It encompasses our ability to explain new concepts in terms of already-familiar ones, to emphasize particular aspects of situations, to generalize, to characterize situations, to explain

The feasibility of derivational analogy as a mechanism for improving problem-solving behavior has been shown for a variety of problem domains by several researchers. However, most of the implemented systems have been empirically evaluated in the restricted context of an already supplied base analog, or on a few isolated examples. In this paper we describe a derivational analogy based system, APU, that synthesizes UNIX shell scripts from a high-level problem specification. APU uses top-down decomposition of problems, employing a hierarchical planner and a layered knowledge base of rules, and is able to speed up the derivation of programs by using derivational analogy. We assume that the problem specification is encoded in the vocabulary used by the rules. We describe APU's retrieval heuristics that exploit this assumption to automatically retrieve a good analog for a target problem from a case library, as well as its replay algorithm that enables it to effectively reuse the sol...

The last few years have witnessed a remarkable expansion of research in machine learning. The field has gained an unprecedented popularity, several new areas have developed, and some previously established areas have gained new momentum. While symbolic methods, both empirical and knowledge-intensive, in particular, inductive concept learning and explanation-based methods, continued to be exceedingly active (Parts 2 and 3 of the book, respectively), sub-symbolic approaches, especially neural networks, have experienced tremendous growth (Part 5). Unlike past efforts that concentrated on single learning strategies, the new trend has been to integrate different strategies, and to develop cognitive learning architectures (Part 4). There has been an increasing interest in experimental comparisons of various methods, and in theoretical analyses of learning algorithms. Researchers have been sharing the same data sets, and have applied their techniques to the same problems in order to understand relative merits of different methods. Theoretical investigations have brought new insights into the complexity of learning processes (Part 6).

Abstract. This paper defines and analyses a computational model of similarity which detects analogies between objects based on conceptual descriptions of them, constructed from classification, generalization relations and attributes. Analogies are detected(elaborated) by functions which measure conceptual distances between objects with respect to these semantic modelling abstractions. The model is domain independent and operational upon objects described in non uniform ways. It doesn’t require any special forms of knowledge for identifying analogies and distinguishes the importance of distinct object elements. Also, it has a polynomial complexity. Due to these characteristics, it may be used in complex tasks involving intra or inter-domain analogical reasoning. So far the similarity model has been applied in the domain of software engineering. First, to support the specification of software requirements by analogical reuse and second, to enable the integration of requirements specifications, generated by the multiple agents involved in information system development. Details of these applications can be found in sited references. Also, we have conducted an empirical evaluation of: (i) the consistency of the estimates generated by the model against human intuition about similarity and (ii) its recall performance in tasks of analogi-cal retrieval, the results of which are presented in this paper. 1.