Understanding analogical reasoning : viewpoints from psychology and related disciplines

Analogy and metaphor have a long history of study in linguistics, education, philosophy and psychology. Consensus over what analogy is or how analogy functions in language and thought, however, has been elusive. This paper, the first in a two part series, examines these various research traditions, attempting to bring out major lines of agreement over the role of analogy in individual human experience. As well as being a general literature review which may be helpful for newcomers to the study of analogy, this paper attempts to extract from these literatures existing theories, models and concepts which may be interesting or useful for computational studies of analogical reasoning

The relation between language and theory of mind in development and evolution

Considering the close relation between language and theory of mind in development and their tight connection in social behavior, it is no big leap to claim that the two capacities have been related in evolution as well. But what is the exact relation between them? This paper attempts to clear a path toward an answer. I consider several possible relations between the two faculties, bring conceptual arguments and empirical evidence to bear on them, and end up arguing for a version of co-evolution. To model this co-evolution, we must distinguish between different stages or levels of language and theory of mind, which fueled each other’s evolution in a protracted escalation process

Interpretation of Natural-language Robot Instructions: Probabilistic Knowledge Representation, Learning, and Reasoning

A robot that can be simply told in natural language what to do -- this has been one of the ultimate long-standing goals in both Artificial Intelligence and Robotics research. In near-future applications, robotic assistants and companions will have to understand and perform commands such as set the table for dinner'', make pancakes for breakfast'', or cut the pizza into 8 pieces.'' Although such instructions are only vaguely formulated, complex sequences of sophisticated and accurate manipulation activities need to be carried out in order to accomplish the respective tasks. The acquisition of knowledge about how to perform these activities from huge collections of natural-language instructions from the Internet has garnered a lot of attention within the last decade. However, natural language is typically massively unspecific, incomplete, ambiguous and vague and thus requires powerful means for interpretation. This work presents PRAC -- Probabilistic Action Cores -- an interpreter for natural-language instructions which is able to resolve vagueness and ambiguity in natural language and infer missing information pieces that are required to render an instruction executable by a robot. To this end, PRAC formulates the problem of instruction interpretation as a reasoning problem in first-order probabilistic knowledge bases. In particular, the system uses Markov logic networks as a carrier formalism for encoding uncertain knowledge. A novel framework for reasoning about unmodeled symbolic concepts is introduced, which incorporates ontological knowledge from taxonomies and exploits semantically similar relational structures in a domain of discourse. The resulting reasoning framework thus enables more compact representations of knowledge and exhibits strong generalization performance when being learnt from very sparse data. Furthermore, a novel approach for completing directives is presented, which applies semantic analogical reasoning to transfer knowledge collected from thousands of natural-language instruction sheets to new situations. In addition, a cohesive processing pipeline is described that transforms vague and incomplete task formulations into sequences of formally specified robot plans. The system is connected to a plan executive that is able to execute the computed plans in a simulator. Experiments conducted in a publicly accessible, browser-based web interface showcase that PRAC is capable of closing the loop from natural-language instructions to their execution by a robot

Humphry Davy and the problem of analogy

This article forms part of a larger project, titled “Poetical Matter” and partly funded by a British Academy Mid-Career Fellowship, which examines the exchange of methods, theories, and language between poetry and the physical sciences in the nineteenth centuryAnalogy, the comparison of one set of relations to another, was essential to Humphry Davy’s understanding of chemistry. Throughout his career, Davy used analogical reasoning to direct and to interpret his experimental analyses of the chemical reactions between substances. In his writing, he deployed analogies to organise and to explain his theories about the relations between physical processes and between the properties of different chemical elements and compounds. But Davy also regularly expressed two concerns about analogical comparison: first, that it was founded not on the rational interpretation of facts but on imaginative speculation; and second, that it was a kind of rhetoric, the persuasiveness of which depended not on material evidence but on misleading figures of speech. This article discusses the influences that informed Davy’s ambivalent assessment of the value of analogy, and it examines the distinct yet overlapping ways in which this assessment was expressed in his notebooks, his lectures and treatises on chemistry, his philosophical writings, and his poetry.PostprintPeer reviewe

Humphry Davy and the Problem of Analogy

Analogy, the comparison of one set of relations to another, was essential to Humphry Davy’s understanding of chemistry. Throughout his career, Davy used analogical reasoning to direct and to interpret his experimental analyses of the chemical reactions between substances. In his writing, he deployed analogies to organise and to explain his theories about the relations between physical processes and between the properties of different chemical elements and compounds. But Davy also regularly expressed two concerns about analogical comparison: first, that it was founded not on the rational interpretation of facts but on imaginative speculation; and second, that it was a kind of rhetoric, the persuasiveness of which depended not on material evidence but on misleading figures of speech. This article discusses the influences that informed Davy’s ambivalent assessment of the value of analogy, and it examines the distinct yet overlapping ways in which this assessment was expressed in his notebooks, his lectures and treatises on chemistry, his philosophical writings, and his poetry

Change and planning in chance discovery.

The discovery of risks and opportunities, known collectively as chances, can have a significant impact on decision making. Chances (risks or opportunities) can be discovered from our daily observations and background knowledge. A person can easily identify chances in a news article. In doing so, the person combines the new information in the article with some background knowledge. Hence, we develop a deductive system to discover relative chances with respect to a particular chance seeker. A chance discovery system that uses a general purpose knowledge base and specialized reasoning algorithms is proposed. The thesis evaluates the implementation of this chance discovery system and discusses the achievements and limitations of its elements, such as Natural Language Processing Tool, Knowledge Entry Tool, Inference Engine and Planner. Finally, A case study about a virtual transportation planning domain implemented using SHOP planner is presented. Example chances are detected in this domain. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .W89. Source: Masters Abstracts International, Volume: 44-03, page: 1418. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Making mathematics on paper : constructing representations of stories about related linear functions

This dissertation takes up the problem of applied quantitative inference as a central question for cognitive science, asking what must happen during problem solving for people to obtain a meaningful and effective representation of the problem. The core of the dissertation reports exploratory empirical studies that seek to answer the descriptive question of how quantitative inferences are generated, pursued, and evaluated by problem solvers with different mathematical backgrounds. These are framed against a controversy, described in Chapter 2, over the theoretical and empirical validity of current cognitive science accounts of problems, solutions, knowledge, and competent human activity outside of laboratory or school settings.Chapter 3 describes a written protocol study of algebra story problem solving among advanced undergraduates in computer science. A relatively open-ended interpretive framework for "problem-solving episodes" is developed and applied to their written solution attempts. The resulting description of problem-solving activities gives a surprising image of competence among an important occupational target for standard mathematics instruction.Chapter 4 follows these results into detailed verbal problem-solving interviews with algebra students and teachers. These provide a comparison across settings and levels of competence for the same set of problems. The results corroborate similar generative activities outside the standard formalism of algebra across levels of competence. Notable among these nonalgebraic problem-solving activities are "model-based reasoning tactics," in which people reason about quantitative relations in terms of the dimensional structure of functional relations described in the problem. These tactics support different activities within surrounding solution attempts and usually describe "states" in the problem's situational structure.Chapter 5 holds these activities accountable to local combinations of notation and quantity, reinterpreting results for model-based reasoning in an ecological analysis of material designs for constructing and evaluating quantitative inferences. This analysis brings forward important relations between what material designs afford problem solvers and the complexity of episodic structure observed in their solution attempts. The dissertation closes with a reappraisal of the relationship between knowledge, person, and setting and, I will argue, puts us on a more promising track for a descriptively adequate theoretical account of constructing mathematical representations that support applied quantitative inference

What working memory is for

Glenberg focuses on conceptualizations that change from moment to moment, yet he dismisses the concept of working memory (sect. 4.3), which offers an account of temporary storage and on-line cognition. This commentary questions whether Glenberg's account adequately caters for observations of consistent data patterns in temporary storage of verbal and visuospatial information in healthy adults and in brain-damaged patients with deficits in temporary retention.</jats:p