A Short Introduction to Computational Trends in Analogical Reasoning

Prade, Henri; Richard, Gilles

doi:10.1007/978-3-642-54516-0_1

Cited by 16 publications

(14 citation statements)

References 137 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, we find the original goal of AI distributed over three, partially overlapping, communities: Cognitive Systems [71,72,73,74], where research focusses on specific, implemented simulation models based on standard AI methods, Artificial General Intelligence [75,76], where researchers aim at autonomous, artificial systems that show human-like performance over a broad range of domains, and Cognitive Modelling, where researchers build simulation models based on cognitive theories and empirically compare similarity between models and human performance in specific tasks [77,78,79]. A domain extensively researched in cognitive science is analogy making with numerous approaches from cognitive systems as well as from cognitive modelling [80,81]. Modelling mental operations also includes brain models and simulations of neurological processes [82], but these involve more than just behaviour (i.e., brain neurophysiology and imaging).…”

Section: Simulating Mental Operations: Cognitive Systems and Cognitivmentioning

confidence: 99%

Computer models solving intelligence test problems: Progress and implications

Hernández-Orallo

Martínez-Plumed

Schmid

et al. 2016

Artificial Intelligence

View full text Add to dashboard Cite

ElsevierHernández Orallo, J.; Martínez Plumed, F.; Schmid, U.; Siebers, M.; Dowe, DL. (2016 AbstractWhile some computational models of intelligence test problems were proposed throughout the second half of the XXth century, in the first years of the XXIst century we have seen an increasing number of computer systems being able to score well on particular intelligence test tasks. However, despite this increasing trend there has been no general account of all these works in terms of how they relate to each other and what their real achievements are. Also, there is poor understanding about what intelligence tests measure in machines, whether they are useful to evaluate AI systems, whether they are really challenging problems, and whether they are useful to understand (human) intelligence. In this paper, we provide some insight on these issues, in the form of nine specific questions, by giving a comprehensive account of about thirty computer models, from the 1960s to nowadays, and their relationships, focussing on the range of intelligence test tasks they address, the purpose of the models, how general or specialised these models are, the AI techniques they use in each case, their comparison with human performance, and their evaluation of item difficulty. As a conclusion, these tests and the computer models attempting them show that AI is still lacking general techniques to deal with a variety of problems at the same time. Nonetheless, a renewed attention on these problems and a more careful understanding of what intelligence tests offer for AI may help build new bridges between psychometrics, cognitive science, and AI; and may motivate new kinds of problem repositories.

show abstract

Section: Simulating Mental Operations: Cognitive Systems and Cognitivmentioning

confidence: 99%

Computer models solving intelligence test problems: Progress and implications

Hernández-Orallo

Martínez-Plumed

Schmid

et al. 2016

Artificial Intelligence

View full text Add to dashboard Cite

show abstract

“…There is a huge literature on computational models of analogical reasoning with applications in different areas. For an introductory overview of computational trends in analogical reasoning, the reader is referred to Prade and Richard' bibliographical study . The use of analogical proportions to solve geometric quizzes is just a particular instance of analogical reasoning.…”

Section: Related Workmentioning

confidence: 99%

Constructive Solving of Raven's IQ Tests with Analogical Proportions

Beltran

Prade

Richard

2016

Int. J. Intell. Syst.

Self Cite

View full text Add to dashboard Cite

The paper shows that a Boolean logic modeling of analogical proportions can serve as a basis for solving quizzes as well as a common and popular type of IQ tests, namely Raven's progressive matrices. They are nonverbal tests supposedly measuring general intelligence. A 3 × 3 Raven matrix exhibits eight geometric pictures displayed as its eight first cells: the remaining ninth cell is empty. In these tests, a set of candidate pictures is also given among which the subject is asked to identify the solution. In this paper, we investigate a general approach allowing to automatically solve Raven's progressive matrices tests. The approach is based on a logical view of analogical proportions, i.e., statements of the form "A is to B as C is to D." We assume that analogical proportions hold between the rows and between the columns of the Raven's matrix. This view can be applied to a feature-based description of the pictures but also, in a number of cases, to a very low level representation, i.e., the pixel level. It appears that the analogical proportion reading just amounts here to a recopy of patterns of feature values that already appear in the data, after checking that there is no conflicting patterns. Implementing this principle, our algorithm builds up the ninth picture, without the help of any set of candidate solutions, and only on the basis of the eight known cells of the Raven matrices. A comparison with other approaches is provided. The ability to construct the missing picture without relying on candidate solutions is a distinctive feature of our work. Moreover, we emphasize the general principle underlying the approach that offers a simple and uniform mechanism applicable to the tests. At this step, the paper makes no claim about the cognitive validity of the approach with respect to the way humans solve such tests. C 2016 Wiley Periodicals, Inc.Indeed, a and b are supposed to be the values of a given binary feature for two distinct items. Then, a ∧ b holds if the two items have this feature in common, a ∧ b holds if none of the items has this feature, a ∧ b holds if the first item has the feature alone, and vice versa for a ∧ b.Generally speaking, a logical proportion 15 is a conjunction of two equivalences between such indicators. Among the 120 distinct logical proportions that exist, the In the above definition of an analogical proportion, a, b, c, and d belong to the same domain, namely, the set of truth values {0, 1}. We may need a view of analogy where a, b, c, and d do not belong to a unique universe. An easy way to do this is to state that an analogical proportion holds between a, b, c, and d as soon as there exists a relation R such that R(a, b) = R (c, d)

show abstract

“…How, then, can an intelligent agent address any novel problem or situation? In analogical thinking, the intelligent agent addresses novel problems and situations by analogy to its experiences with similar, familiar problems and situations (Hofstadter, 1996; Holyoak & Thagard, 1996; Dunbar, 1997; Gentner & Markman, 1997; Clement, 2008; Prade & Gilles, 2014). AI research on analogical design has ranged from model-based analogies (Qian & Gero, 1996; Bhatta & Goel, 1997) to visual analogies (Davies et al, 2009).…”

Section: Broader Context Of Analogical Thinkingmentioning

confidence: 99%

On the benefits of digital libraries of case studies of analogical design: Documentation, access, analysis, and learning

Goel

Zhang

Wiltgen

et al. 2015

AIEDAM

View full text Add to dashboard Cite

Digital libraries of case studies of analogical design have been popular since their advent in the early 1990s. We consider four benefits of digital libraries of case studies of analogical design in the context of biologically inspired design. First, a digital library affords documentation. The 83 case studies in our work come from 8 years of extended, collaborative design projects in an interdisciplinary class on biologically inspired design. Second, a digital library provides on-demand access to the case studies. We describe a web-based library of case studies of biologically inspired design called the Design Study Library (DSL). Third, a compilation of case studies supports analyses of broader patterns and trends. As an example, an analysis of DSL's case studies found that environmental sustainability was a major factor in about a third of the case studies and an explicit design goal in about a fourth. Fourth, a digital library of case studies can support analogical learning. Preliminary results from an exploratory study indicate that DSL may support novice learning about the processes of biologically inspired design.

show abstract

A Short Introduction to Computational Trends in Analogical Reasoning

Cited by 16 publications

References 137 publications

Computer models solving intelligence test problems: Progress and implications

Computer models solving intelligence test problems: Progress and implications

Constructive Solving of Raven's IQ Tests with Analogical Proportions

On the benefits of digital libraries of case studies of analogical design: Documentation, access, analysis, and learning

Contact Info

Product

Resources

About