Analogy in a general-purpose reasoning system

Advances in connectionist and neural computation …, 1994

Analogy-Based (or Analogical) and Case-Based Reasoning (ABR and CBR) are two similar problem solving processes based on the adaptation of the solution of past problems for use with a new analogous problem. In this paper we review these two processes and we give some real world examples with emphasis to the field of Medicine, where one can find some of the most common and useful CBR applications. We also underline the differences between CBR and the classical rule-induction algorithms, we discuss the criticism for CBR methods and we focus on the future trends of research in the area of CBR.

Proceedings of the third international conference on Industrial and engineering applications of artificial intelligence and expert systems - IEA/AIE '90, 1990

The research described in this paper addresses the problem of integrating analogical reasoning and argumentation into a natural language understanding system. We present an approach to completing an implicit argument-by-analogy as found in a natural language editorial text. The transformation of concepts from one domain to another, which is inherent in this task, is a complex process requiring basic reasoning skills and domain knowledge, as well as an understanding of the structure and use of both analogies and arguments. The integration of knowledge about natural language understanding, argumentation, and analogical reasoning is demonstrated in a proof of concept system called ARIEL. ARIEL is able to detect the presence of an analogy in an editorial text, identify the source and target components, and develop a conceptual representation of the completed analogy in memory. The design of our system is modular in nature, permitting extensions to the existing knowledge base and making the argumentation and analogical reasoning components portable to other understanding systems.

Computer Science and Information Systems, 2015

In this paper an approach in realization of analogy-based reasoning in semantic networks is presented. New semantic model, called Active Semantic Model (ASM), was used. Core of the process is performed by ASM's association (semantic relation) plexus upgrading procedure based on recognition and determining similarity between association plexuses. Determining similarity between association plexuses is performed by recognition of topological analogy between association plexuses. ASM responds to unpredicted input by upgrading new association plexus modeled on remainder of the context whose subset is recognized as topologically analogous association plexus.

International Journal of Digital Content Technology and its Applications, 2010

Analogy reasoning is one of the most important reasoning means of human thinking. How to implement analogy reasoning automatically with computers has been a hot topic in artificial intelligence and psychology. This paper proposes a mathematic model for analogy reasoning over semantic link network (SLN). We propose a reliable analogy theorem over SLN based on the category theory and some algorithms have been developed to implement analogical reasoning by constructing a semantic functor between SLNs. Meanwhile, we also discuss some analogy conjecture models and algorithms which may be unreliable but useful to giving some suggestions for solving a given problem. A study cases is proposed to show the validity and efficiency of the proposed theorem and the algorithms.

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)

2014

In this paper an approach in realization of analogy-based reasoning in semantic network is presented. New semantic model, called Active Semantic Model (ASM), was used. Core of the process is performed by ASM’s association (semantic relation) plexus upgrading procedure which is based on recognition and determining the similarity of association plexuses. Determining the similarity of association plexuses is performed by the recognition of topological analogy between association plexuses. ASM responds to unpredicted input by upgrading the new association plexus modeled on remainder of the context whose subset is recognized as topologically analogous association plexus.

Systems and Computers in Japan, 2000

If a knowledge base does not have all of the necessary clauses for reasoning, ordinary hypothetical reasoning systems cannot explain observations. In this case, it is necessary to explain such observations by abductive reasoning, supplemental reasoning, or approximate reasoning. The inference in this paper explains the observation by supplementing missing knowledge with reference to similar knowledge when an observation cannot be explained because necessary knowledge is lacking. However, it is somewhat difficult to find clauses to explain an observation without hints. Therefore, an abductive strategy (CMS) is used to find missing clauses. A piece of knowledge which is similar to the missing knowledge is sought in the knowledge base and mapped to the knowledge in the same problem domain as the missing knowledge. Then the observation is explained by generated hypotheses similar to the knowledge in the knowledge base.

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.

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.

palm.mindmodeling.org

This paper presents a series of simulations performed with the AMBR model that demonstrate how deduction, induction, and analogy can emerge from the interaction of several simple mechanisms. First, a case of deductive reasoning is demonstrated when a problem is solved based on general knowledge. The system represents the target in different ways depending on the goal, and different solutions are generated. Second, the constructed solutions of the problems are remembered and later on used as a base for remote analogy. Finally, on the basis of the analogy made, a generalized solution of the class of problems is induced. One important characteristic of the model is that representation of the task, problem-solving, and learning are not viewed as separate modules. Instead, they are different aspects of one and the same joined work of the basic mechanisms of the architecture.

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.

2003

The goal of the work reported here is to capture the commonsense knowledge of non-expert human contributors. Achieving this goal will enable more intelligent human-computer interfaces and pave the way for computers to reason about our world. In the domain of natural language processing, it will provide the world knowledge much needed for semantic processing of natural language. To acquire knowledge from contributors not trained in knowledge engineering, I take the following four steps: (i) develop a knowledge representation (KR) model for simple assertions in natural language, (ii) introduce cumulative analogy, a class of nearest-neighbor based analogical reasoning algorithms over this representation, (iii) argue that cumulative analogy is well suited for knowledge acquisition (KA) based on a theoretical analysis of effectiveness of KA with this approach, and (iv) test the KR model and the effectiveness of the cumulative analogy algorithms empirically. To investigate effectiveness of cumulative analogy for KA empirically, Learner, an open source system for KA by cumulative analogy has been implemented, deployed, 1 and evaluated. Learner acquires assertion-level knowledge by constructing shallow semantic analogies between a KA topic and its nearest neighbors and posing these analogies as natural language questions to human contributors. Suppose, for example, that based on the knowledge about "newspapers" already present in the knowledge base, Learner judges "newspaper" to be similar to "book" and "magazine." Further suppose that assertions "books contain information" and "magazines contain information" are also already in the knowledge base. Then Learner will use cumulative analogy from the similar topics to ask humans whether "newspapers contain information."

Dialogue

Despite its importance in various fields, analogical reasoning has not yet received a unified formal representation. Our contribution proposes a general scheme of inference that is compatible with different types of logic (deductive, probabilistic, non-monotonic). Firstly, analogical assessment precisely defines the similarity of two objects according to their properties, in a relative rather than absolute way. Secondly, analogical inference transfers a new property from one object to a similar one, thanks to an over-hypothesis linking two sets of properties. The belief strength in the conclusion is then directly related to the belief strength in this meta-hypothesis.

2000

A formal model of analogy is introduced in the logic programming setting, and an analogical reasoning program (called DIANA, i.e. Declarative Inference by ANAlogy) is developed in accordance with precise procedural and declarative semantics. Given the source and target domains of analogy as two logic programs Ps and Pt, together with a specification S of the analogical correspondence between predicate symbols, atoms involving these symbols are analogically derived from P = Ps U Pt given S, which are not derivable from Ps or Pt or Ps U Pt alone. In this paper, the requirements of the analogical process are first stated. The declarative semantics of analogy is then given, by defining the least analogical model of P as an extension of the classical semantics of Horn clauses. A procedural semantics is also described, in terms of an extension of SLD resolution. Both semantics rely on implicit analogical axioms defining the kind of analogical reasoning envisaged. The implementation of DIANA has been done in Reflective Prolog, a metalogic programming language previously developed by the first two authors. It is shown that analogical axioms can be viewed as an instance of reflection axioms used in Reflective Prolog. By exploiting this feature, the implementation of DIANA is argued to be sound w.r.t, the defined semantics. Examples of analogical reasoning in DIANA are also described. By comparison with the AI literature on analogy, it is claimed that this is the first approach which gives a declarative semantics to analogical reasoning, thanks to the possibility of carrying over in this field the basic logic programming concepts.