Analogy in a general-purpose reasoning system (original) (raw)
2009, Cognitive Systems Research
This paper introduces the various forms of analogy in NARS, a generalpurpose reasoning system. NARS is an AI system designed to be adaptive and to work with insufficient knowledge and resources. In the system, multiple types of inference, including analogy, deduction, induction, abduction, comparison, and revision, are unified both in syntax and in semantics. The system can also carry out relational and structural analogy, in ways comparable to (though different from) that in some other models of analogy, such as Copycat and SME. The paper addresses several theoretical issues in the study of analogy, including the specification and justification of analogy, the context sensitivity of analogy, as well as the role analogy plays in intelligence and cognition.
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Analogy as Integrating Framework for Human-Level Reasoning
2008
Human-level reasoning is manifold and comprises a wide variety of different reasoning mechanisms. So far, artificial intelligence has focused mainly on using the classical approaches deduction, induction, and abduction to enable machines with reasoning capabilities. However, the approaches are limited and do not reflect the mental, cognitive process of human reasoning very well. We contend that analogical reasoning is the driving force behind human thinking and therefore propose analogy as an integrating framework for the variety of human-level reasoning mechanisms.
A framework for arguing from analogy: preliminary results
Human reasoning applies argumentation patterns to draw conclusions about a particular subject. These patterns represent the structure of the arguments in the form of argumentation schemes which are useful in AI to emulate human reasoning. A type of argument schema is that what allow to analyze the similarities and differences between two arguments, to find a solution to a new problem from an already known one. Researchers in the heavily studied field of analogies in discourse have recognized that there is not a full and complete definition to indicate when two arguments are considered analogous. Our proposal presents an initial attempt to formalize argumentation schemes based on analogies, considering a relationship of analogy between arguments. This will contribute to the area increasing such schemes usefulness in Artificial Intelligence (AI), since it can be implemented later in Defeasible Logic Programming (DeLP).
A Logical Approach to Reasoning by Analogy.
We analyze the logical form of the domain knowledge that grounds analogical inferences and generalizations from a single instance. The form of the assumptions which justify analogies is given schematically as the "determination rule", so called because it expresses the relation of one set of variables determining the values of another set. The determination relation is a logical generalization of the different types of dependency relations defined in database theory. Specifically, we define determination as a relation between schemata of first order logic that have two kinds of free variables: (1) object variables and what we call "polar" variables, which hold the place of truth values. Determination rules facilitate sound rule inference and valid conclusions projected by analogy from single instances, without implying what the conclusion should be prior to an inspection of the instance. They also provide a way to specify what information is sufficiently relevant to decide a question, prior to knowledge of the answer to the question. 1
Analogy plays an important role in science as well as in non-scientific domains such as taxonomy or learning. We make explicit the difference and complementarity between the concept of analogical statement, which merely states that two objects have a relevant similarity, and the concept of analogical inference, which relies on the former in order to draw a conclusion from some premises. For the first, we show that it is not possible to give an absolute definition of what it means for two objects to be analogous; a relative definition of analogy is introduced, only relevant from some point of view. For the second, we argue that it is necessary to introduce a background over-hypothesis relating two sets of properties; the belief strength of the conclusion is then directly related to the belief strength of the over-hypothesis. Moreover, we assert the syntactical identity between analogical inference and one case induction despite important pragmatic differences.
Reasoning and learning by analogy: Introduction
American Psychologist, 1997
1. Analogy is a powerful cognitive mechanism that people use to make inferences and learn new abstractions. The history of work on analogy in modern cognitive science is sketched, focusing on contributions from cognitive psychology, artificial intelligence, and philosophy of science. This review sets the stage for the 3 articles that follow in this Science Watch section.(PsycINFO Database Record (c) 2012 APA, all rights reserved)
A Multi-Strategy Approach to Structural Analogy Making
Journal of Intelligent Information Systems, 2017
Analogy is the cognitive process of matching the characterizing features of two different items. This may enable reuse of knowledge across domains , which can help to solve problems. Indeed, abstracting the 'role' of the features away from their specific embodiment in the single items is fundamental to recognize the possibility of an analogical mapping between them. The analogical reasoning process consists of five steps: retrieval, mapping, evaluation , abstraction and re-representation. This paper proposes two forms of an operator that includes all these elements, providing more power and flexibility than existing systems. In particular, the Roles Mapper leverages the presence of identical descriptors in the two domains, while the Roles Argumentation-based Mapper removes also this limitation. For generality and compliance with other reasoning operators in a multi-strategy inference setting, they exploit a simple formalism based on First-Order Logic and do not require any background knowledge or meta-knowledge. Applied to the most critical classical examples in the literature, they proved to be able to find insightful analogies.
Analogy, Deduction and Learning
Proceedings of the 38th Annual Hawaii International Conference on System Sciences
Analogy-based hypothesis generation combined with ontology-based deduction is a promising technique for knowledge discovery and validation. We are using this combined approach to improve the quality of analogy reasoning. This paper is a report of our work in progress in that direction. We will discuss the formal basis and method of the approach from a symbolic machinelearning point of view and propose a generalized model for analogy-based hypothesis generation that allows multi-strategy learning of analogies. We will also present the results of our experiments using this combined approach with the unstructured summary data from the Center for Nonproliferation Studies (CNS) and discuss possible improvements. Finally, we will propose some research issues in order to further develop and deploy this technique.
Large-Scale Analogical Reasoning
Cognitive simulation of analogical processing can be used to answer comparison questions such as: What are the similarities and/or differences between A and B, for concepts A and B in a knowledge base (KB). Previous attempts to use a general-purpose analogical reasoner to answer such questions revealed three major problems: (a) the system presented too much information in the answer, and the salient similarity or difference was not highlighted; (b) analogical inference found some incorrect differences; and (c) some expected similarities were not found. The cause of these problems was primarily a lack of a well-curated KB and, and secondarily, al-gorithmic deficiencies. In this paper, relying on a well-curated biology KB, we present a specific implementation of comparison questions inspired by a general model of analogical reasoning. We present numerous examples of answers produced by the system and empirical data on answer quality to illustrate that we have addressed many of the problems of the previous system.
Varieties of Analogical Reasoning
Motivation -The purpose of this article is to reinvigorate debate concerning the nature of analogy and broaden the scope of current conceptions of analogy. Research approach -An analysis of the history of the concept of analogy, case studies on the use of analogy in problemsolving, cognitive research on analogy comprehension, and a naturalistic inquiry into the various functions of analogy. Findings and Implications -Psychological theories and computational models have generally relied on: (a) A single set of ontological concepts (a property called "similarity" and a structuralist categorization of types of semantic relations) (b) A single form category (i.e., the classic four-term analogy), and (c) A single set of morphological distinctions (e.g., verbal versus pictorial analogies). The taxonomy presented here distinguishes functional kinds of analogy, each of which presents an opportunity for research on aspects of reasoning that have been largely unrecognized. Originality/Value -The various functional kinds of analogy will each require their own treatment in macrocognitive theories and computational models. Take away message -The naturalistic investigation of the functions of analogy suggests that analogy is a macrocognitive phenomenon derivative of number of supporting processes, including the apperception of resemblances and distinctions, metaphor, and the balancing of semantic flexibility and inference constraint.
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