Analogy, Deduction and Learning

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.

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

Uncertainty in Pharmacology (Boston Studies in the Philosophy and History of Science 338), 2020

Analogical arguments are ubiquitous vehicles of knowledge transfer in science and medicine. This paper outlines a Bayesian evidence-amalgamation framework for the purpose of formally exploring different analogy-based inference patterns with respect to their justification in pharmacological risk assessment. By relating formal explications of similarity, analogy, and analog simulation, three sources of confirmatory support for a causal hypothesis are distinguished in reconstruction: relevant studies, established causal knowledge, and computational models.

Cognitive Science, 2017

Making analogies is an important way for people to explain and understand new concepts. Though making analogies is natural for human beings, it is not a trivial task for a dialogue agent. Making analogies requires the agent to establish a correspondence between concepts in two different domains. In this work, we explore a data-driven approach for making analogies automatically. Our proposed approach works with data represented as a flat graphical structure, which can either be designed manually or extracted from Internet data. For a given concept from the base domain, our analogy agent can automatically suggest a corresponding concept from the target domain, and a set of mappings between the relationships each concept has as supporting evidence. We demonstrate the working of this algorithm by both reproducing a classical example of analogy inference and making analogies in new domains generated from DBPedia data.

Studies in Computational Intelligence, 2014

A capacity for analogy is an excellent acid test for the quality of a knowledge-base. A good knowledge-base should be balanced and coherent, so that its high-level generalities are systematically reflected in a variety of lower-level specializations. As such, we can expect a rich, well-structured knowledge-base to support a greater diversity of analogies than one that is imbalanced, disjoint or impoverished. We argue here that the converse is also true: when choosing from a large pool of candidate propositions, in which many propositions are invalid because they are extracted automatically from corpora or volunteered by untrained web-users, we should prefer those that are most likely to enhance the analogical productivity of the knowledge-base. We present a simple and efficient means of finding potential analogies within a large knowledge-base, using a corpus-constrained notion of pragmatic comparability rather than the typically less-constrained notion of semantic similarity. This allows us to empirically demonstrate, in the context of a substantial knowledge-base of simple generalizations automatically extracted from the Google n-grams, that knowledge acquisition proceeds at a significantly faster pace when candidate additions are prioritized according to their analogical potential.

In this paper, we propose a new approach to generate analogy questions of the form " A is to B as ... is to ? " from ontologies. Analogy questions are widely used in multiple-choice tests such as SATs and GREs and are used to assess student's higher cognitive abilities. The design, implementation and evaluation of the new approach are presented in this paper. The results show that mining ontologies for such questions is fruitful.

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.

Dialogue, 2021

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 single case induction despite important pragmatic differences.

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.

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).