Winston, P. H. 1980. Learning and reasoning by analogy. Communications of the ACM 23(12):689-- 703.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....content. Even though metaphor and analogy may be considered to be distinct and separate entities, an obvious byproduct of metaphor is the suggestion of analogy. The process of analogy is that of applying previously gathered knowledge in new situations. Analogy is involved in learning and reasoning [21], and it has been suggested that analogy is a fundamental cognitive process [22] For these reasons, analogy has been an area of interest in cognitive science. Gentner and Gentner [23] conducted empirical experiments that tested the hypothesis that analogies are used in generating inferences ....

....metaphor really such an important phenomenon that it deserves our attention Answer (Chapter 3) In short, yes. Metaphors influence communication and attention processes. Metaphors suggest analogies, and analogy is a fundamental cognitive process [22] that is involved in learning and reasoning [21]. Volumes of active multidisciplinary research are devoted to metaphor [18] According to Veale [18, 19] theories concerning the phenomenon of metaphor can be roughly classified into Literalist and Figuralist positions. Figuralist views are metaphor friendly, and lend support to the idea of ....

P.H. Winston, "Learning and Reasoning by Analogy," in Comm. of the ACM, vol. 23, no. 12, December 1980.

.... learning, and creativity [19, 31, 33] For this reason, a goal from the early days of artificial intelligence has been to build systems able to reason by analogy; early works include Evan s ANALOGY program [15] and Winston s seminal work on finding and exploiting parallels in simple theories [52]. In this paper, we propose a novel statistical approach for finding analogies between images, from the perspective of modeling transformations that are not just shifts, scales, and rotations but rather mappings of one sort of object or relation into another. Recently, a number of applications of ....

P.H. Winston. Learning and Reasoning by Analogy. Communications of the ACM, (23) 12, December 1980.

....Our present opinion is that both approaches make sense. One important factor here is whether the dialectical layer is embedded in the procedural layer. Another important factor is whether a reasoning form is used to justify a conclusion or not. For instance, some uses of analogy concern learning [ Winston, 1980 ] while other uses concern justi cation (as in much AI Law work on case based 6 reasoning) One thing is especially important: if non deductive arguments are admitted at the logical layer, then the dialectical layer should provide for ways to attack the link between their premises and ....

P.H. Winston. Learning and reasoning by analogy. Communications of the ACM, 23:689-703, 1980. 40

....somewhat more conducive to the integration of metaphor, these typically are not designed with the use of metaphor in mind, and the implicit nature of many metaphors makes them easy for developers to ignore. This is an unfortunate situation since metaphor is central to language [17, 18] cognition [19, 20, 21, 22], and diverse aspects of computer science [23, 24, 25] The rube paradigm seeks, in part, to change this situation by conceptually and visually integrating metaphors in M P. Further, rube encourages developers to devise their own metaphors in accordance with the observation that one of the main ....

P. H. Winston (1980) Learning and Reasoning by Analogy. Comm. of the ACM, 23(12), 689-703.

....geometric analogy problems to analogies in the larger sense. Analogy, broadly stated, is using the relationships in one model to reason about those in another model. The models may be mental models (Johnson Laird, 1983) conceptual graphs (Leishman, 1989) extensible relations representations (Winston, 1980), or some other collections of formal, visual, or natural language statements in two domains. The base model is generalized, or a correspondence made between parts of the base model and parts of the target model, so that new statements can be made about the parts of the target model. For geometric ....

Winston, Patrick H. Learning and reasoning by analogy. Communications of the ACM, 23(12), December 1980.

....language. Introduction Most theoretical and computational accounts of analogical reasoning posit transfer of relational knowledge. Causal and functional relationships in particular have been the focus of many theories (see Holyoak Thagard 1997, Bhatta Goel 1997, Falkenhainer et al. 1990, and Winston 1980 for examples) Consider, for example, a traditional account (Gick Holyoak 1996) of Duncker s radiation problem (1926) In this task, experimental participants read a story in which a problem is solved: A general with a large army wants to overthrow a dictator who lives in a fortress. All roads ....

Winston, P.H. (1980). Learning and Reasoning by Analogy.

....between case facts as a similarity metric. Two cases are structurally similar if objects in the cases can be placed into correspondence so that relations also correspond (Holyoak and Thagard, 1989) This approach, which was originally suggested as a model of precedential legal reasoning in (Winston, 1980), was used in BRAMBLE (Bellairs, 1989) The motivation for use of structural similarity as a similarity metric is the hypothesis that relevant similarity between cases is a function of relational commonalities independent of the objects in which those relations are embedded. Gentner, 1989) ....

Winston, P. H. (1980). Learning and reasoning by analogy. Communications of the ACM, 23(12).

....functional definitions. Brady et al. 8] addressed the interplay of planning and reasoning, and the functional significance of higher order structures in the organization of the recovered information. Connell et al. 14] described a system, based on a modified version of Winston s Analogy program [61] which used semantic nets to investigate the relation between form and function. Stark et al. 52, 54] focused on the classification of CAD models of chairs. In some more current work, Green et al. 21] have extended the previous system to classify not only other types of furniture but also to ....

P.H. Winston. Learning and Reasoning by Analogy. Communication of ACM, 23(12):689--703, 1980.

.... pursued intensively, probably because of its difficulty, which follows in turn from the difficulty of finding good descriptions of how humans make analogies [Campbell,86] One of the early programs to use analogy to solve geometric problems is by Evans [Evans,63] A more recent work by Winston [Winston,80] demonstrated the use of learning by analogy. In this work he emphasised the usefulness of transferring knowledge from one domain to another. For example, since there is an analogy between resistors and water pipes, we can learn Ohm s law by referring to resistance to fluid flows that we have ....

....a better and more exact response to the problem. For example, there is a wealth of information on the performance characteristics of subsystems available in the literature and it is aimed to catalogue some of these and permit searching and reasoning by matching features of profile of objects [Winston,80] A second case is to encode characteristics of communications infrastructures profiles [GOSIP,88] for comparisons. 6.4.3. User Interface Interacting with a knowledge based system can be a complex task, especially for users who are not programmers. For example, in our case one needs to master ....

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Winston, P. H., "Learning and Reasoning by analogy," Communications of the ACM, 23(12), (1980).

....indexing (EBI) Barletta Mark, 1988) Since the SBF models provide an explanation of the functioning of a device, like EBG, EBL and EBI, our approach also uses explanations for learning. Further, our work in terms of integrating experience and explanation for learning is also related to Winston s (1980) work on learning by analogy. More importantly, our work is similar in spirit to recent work in model based learning (Dietterich, 1992; Roverso, Edwards Sleeman, 1992) However, our model based approach differs from the other approaches in several ways. The learning tasks in both EBG and EBL ....

....task is to select some subset of the set of features. In contrast, IDEAL knows only about the types of features that are to be used as indices (e.g. structural features) but identifies the exact vocabulary for indices from the SBF models. Moreover, IDEAL learns multiple types of indices. Winston s (1980) work addressed the task of learning causal principles from experience. Dietterich s (1992) work focuses on learning specialized models from general domain models. The task in ASIS (Roverso, Edwards Sleeman, 1992) is learning of abstract models from experiences. The learning strategy and the ....

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Winston, P.H. (1980). Learning and Reasoning by Analogy. Communications of the ACM, 23(12).

....integrates model based learning with similarity based learning (SBL) Specifically, it uses model based approach to learn the indexing features, and SBL to generalize over the learned features. Further, our work in terms of integrating experience and explanation for learning is also related to Winston s [1980] work on learning by analogy. However, Winston [1980] uses the experience and the causal structure in the experience to learn new principles in the context of understanding stories. In contrast, we have shown how experience and explanation can be brought together for learning indices. 9 6.1 ....

....learning (SBL) Specifically, it uses model based approach to learn the indexing features, and SBL to generalize over the learned features. Further, our work in terms of integrating experience and explanation for learning is also related to Winston s [1980] work on learning by analogy. However, Winston [1980] uses the experience and the causal structure in the experience to learn new principles in the context of understanding stories. In contrast, we have shown how experience and explanation can be brought together for learning indices. 9 6.1 Further Research A design case can be indexed not just ....

Winston, P.H. Learning and Reasoning by Analogy, Communications of the ACM, 23(12), 1980. 10

....4 7W; 5 7 e l 7 g s K a G O ;kE 7A . 5 9= r BAu 7 7W; 5 7 e l 7 g s K D F=R Y k # M4V N;W9M N b G k N8 5f G O Ds0F 9 k b G k r 7W; 5 e K BAu 7 7 e l 7 g s rMQ M 8z d BEEv r8 Z 9 k H jK , 3 l GB H i l F (Nc ( P [2] Ey) #K a GJs9p 9 k7W; 5 7 e l 7 g s G O V;kE r7A . 9 kCN 1 W (0J9 K a G OC1 KCN 1 H8F V) NB8: r2 Dj 7 = l K4p E ;k E 7A .5 9= NM 8z r8 Z 9 k #0J9 3 N2 Dj N0U 5A5Z S 7 e l 7 g s7k2L K D F=R Y k # y (Da ) Da N6R=89g (ItJ, 89gA4BN i J k=89g) 4.1 ....

P.H. Winston. Learning and reasoning by analogy. CACM (Communications of the ACM), Vol. 23, No. 12, pp. 689--703, 1980.

....of structural models of specific situations while IDeAL discovers models of prototypical devices, physical principles, and processes. Explanations in Learning: The proposal that learning from experience is facilitated by explanations of specific experiences dates at least as far back as Winston [1980]. Winston s model assumed knowledge of what is the concept being learned and relied on information concerning whether an example is a positive instance or negative instance of the concept. Our approach is similar to Winston s later models [1982; 1986] that show that learning can be done by ....

P. Winston. Learning and Reasoning by Analogy. Communications of the ACM, 23(12):689--703, 1980.

....reasoning effort. The importance of analogical reasoning has been acknowledged by many researchers. For example, the use of analogy in problem solving and learning was proposed by, among others, Carbonell (Carbonell and Michalski, 1983a; Carbonell and Michalski, 1983b; Carbonell, 1986) Winston (Winston, 1980), Falkenhainer et al. Falkenhainer and Forbus, 1989) Kedar Cabelli (Kedar Cabelli, 1988) Burstein (Burstein, 1986) and Kodratoff (Kodratoff, 1988) 1 One of the above (Winston, 1984) demonstrates the importance of past experience as being one element in a hypothetical intelligent machine. ....

....(Kling, 1971) created a program for constraining axioms. The axioms assist in proving a new theorem by analogy with a similar proof in an analogous theorem. The input consists of the target theorem to be proved, an analogous base theorem as well as its proof in this case. Winston (Winston, 1975; Winston, 1980; Winston, 1984) used similarities between situations such as stories and physical experiments, in learning new concepts by analogy. Winston argued that much of human thinking is done by analogy. When a person faces a new situation, he she tries to recall another situation which was similar in ....

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Winston, P. (1980). Learning and reasoning by analogy. Commun. ACM, 23(12):689-- 703.

....is organized around belief systems. Their major operations include syntactic analysis using the current parser (see section 2.4.5) semantic reference using the constraint interpreting reference system, sentential constraint posting, and question answering. Other operations include tracking 18 Winston (1980; 1984) and Katz (1980; Katz Winston, 1982) used Katz s deep structure transformational parser to build representations in Winston s ternary relation frame system. RELATUS is a descendant of that system. RELATUS differs, inter alia, from the Winston representation system because it adopts a ....

....analogy system between 1980 and 1983. 37 By 1984, the author was able to parse and fully represent in RELATUS both the Hungary story and the 1968 Soviet Intervention in Czechoslovakia. The texts each run about ten pages and create about 6000 graph structure nodes each. The coding employed Winston s (1980) conventions, making liberal use of the connective because to create causal relations between clauses. These connectives allowed the Winston analogy system, and later the RELATUS analogy routines (Mallery Hurwitz, 1987) to follow causal relations and formulate analogies. This coding was ....

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P. H. Winston, [1980], "Learning and Reasoning by Analogy," Communications of the ACM, December, 1980, 23(12).

....in P 2 , and fi i and fi 0 i are analogous for each i (1 i n) Then analogical reasoning is to derive a ground atom ff 0 in P 2 which is analogous to ff. We follow the principle of analogical reasoning by Haraguchi and Arikawa [7] shown in Figure 2, that is based on Polya [14] and Winston [18, 19]. P 1 : fi 1 ; fi n ff m m (similarity ) P 2 : fi 0 1 ; fi 0 n ff 0 Figure 2: The principle of analogical reasoning. The in Figure 2 denotes an analogy which gives a similarity between P 1 and P 2 . In this paper, we take an analogy as a partial identity as defined ....

P. H. Winston. Learning and reasoning by analogy. Communications of ACM, Vol. 23, pp. 689--703, 1980.

....However, in spite of this, the contrast model has been applied successfully to the modelling of similarity judgements of schematic faces, geometric faces and feature definitions of concepts. It has also been used as a method for inductive learning in the context of analogical reasoning (see Winston, 1980). Similarity 4 O Sullivan Keane A B U A B B A A B Figure 1: Contrasting Sets. Relationships between sets A and B According to the contrast models any given similarity computation, s(a,b) may be defined as a measure of the similarity of a to b for all distinct a, b in U. This similarity of a ....

Winston, P. H. (1980) Learning and reasoning by analogy. Communications of the ACM, pages 689¾703.

....changes to the variables characterizing P new . In general, analogical transfer requires the use of generic abstractions, where the abstractions typically express the structure of relationships between generic types of objects and processes. The analogical reasoning literature in AI (e.g. [Winston 1980]) in cognitive psychology (e.g. Gick and Holyoak 1983] and in cognitive science (e.g. Falkenhainer, Forbus and Gentner 1989] all suggests that the generic abstractions are not merely abstractions over features of objects, but that they capture the relational structure among objects and ....

P. Winston. Learning and Reasoning by Analogy. CACM, 23(12)689-703, 1980.

....kinds of human processing[33,36] and dealing effectively with analogical reuse. 2. A General Review of Similarity Similarity is described as a relation determined by some flexible comparison between the distinct constituents of two entities (i. e situations, cases, natural or nominal kind objects)[25,31,33,36,38,40]. From a quantitative viewpoint, the result of this comparison may be interpreted in two ways: i. as a measure of closeness in some abstract space[25,36] and ii. as a probability that the objects under comparison, would resemble each other even if their possibly missing constituents were ....

Winston P., Learning and Reasoning by Analogy, Communications of the ACM, 23(12), December 1980

....actions. He concludes that the representation of knowledge requires improvement, perhaps along the lines of conceptual dependency. Further, Tanaka believes that precedent logics require further conceptual clarification. He suggests that similarity measures could be improved along lines found in Winston s (1977, 1980) computational models of analogy. Mefford (1984, 1986a) reports on a program that draws historical analogies to determine responses of the Soviet Union to crises in Eastern Europe, specifically the Czechoslovakian crisis of 1968. The program matches histories against cases to assemble composite ....

P. H. Winston, [1980], "Learning and Reasoning by Analogy," Communications of the ACM, December, 1980, 23(12).

....well known knowledge in the base domain on the target domain by using the analogy. Thus, it is an essential point for analogical reasoning to compute an analogy which maps from a base domain to a target domain. Then, Many authors have extensively studied analogical reasoning from this point of vew [1, 5, 6, 11, 16, 17]. However, there often arises a problem of combinatorial explosion in computing analogies [3] 3 This work is partly supported by Grant in Aid for Scientific Research on Priority Areas from the Ministry of Education, Science and Culture, Japan. y JSPS Fellowship for Japanese Junior ....

....1 ; fi n hold in P 2 , and ff i and fi i (1 i n) are analogous. Then, analogical reasoning is to derive a fact fi in P 2 which is analogous to ff. Thus, we follow the principle of analogical reasoning by Haraguchi and Arikawa [6] shown in Figure 1 which is based on Polya [11] and Winston [16, 17]. P 1 : ff 1 ; ff n ff m similarity m P 2 : fi 1 ; fi n fi Figure 1: The principle of analogical reasoning. Then, in Figure 1 denotes an analogy which gives a similarity between the base and the target. Analogical reasoning is carried out by projecting some of the base ....

P. H. Winston. Learning and reasoning by analogy. Communications of ACM, Vol. 23, pp. 689--703, 1980.

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Winston, P. H. 1980. Learning and reasoning by analogy. Communications of the ACM 23(12):689-- 703.

No context found.

Winston, Patrick H. 1980. Learning and Reasoning by Analogy, Communications of the Association for Computing Machinery, 23:12. 1980.

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P. Winston, "Learning and reasoning by analogy," Communication of ACM, vol. 23, no. 12, Dec. 1980.

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Winston, P. H., "Learning and Reasoning by Analogy", C,qCg[, 23, no. 12, 689-703, 1980.

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