Evaluating Case Selection Algorithms for Analogical Reasoning Systems

AbstractThis paper presents a framework for the interactions between the processes of mapping and rerepresentation within analogy making. Analogical reasoning systems for use in design tasks require representations that are open to being reinterpreted. The framework, interpretation-driven mapping, casts the process of constructing an analogical relationship as requiring iterative, parallel interactions between mapping and interpreting. This paper argues that this interpretation-driven approach focuses research on a fundamental problem in analogy making: how do the representations that make new mappings possible emerge during the mapping process? The framework is useful for both describing existing analogy-making models and designing future ones. The paper presents a computational model informed by the framework Idiom, which learns ways to reinterpret the representations of objects as it maps between them. The results of an implementation in the domain of visual analogy are presented to demonstrate its feasibility. Analogies constructed by the system are presented as examples. The interpretation-driven mapping framework is then used to compare representational change in Idiom to that in three previously published systems.

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Challenges to Engineering Moral Reasoners

10.1093/oso/9780190652951.003.0016 ◽

2017 ◽

Author(s):

Michał Klincewicz

Keyword(s):

Artificial Intelligence ◽

Analogical Reasoning ◽

Knowledge Engineering ◽

Ethical Theories ◽

Philosophical Literature ◽

Engineering Software ◽

Promising Strategy ◽

Symbolic Reasoning ◽

Reasoning Systems ◽

Moral Theories

A combination of algorithms, based on philosophical moral theories and analogical reasoning from standard cases, is a promising strategy for engineering software that can engage in moral reasoning. This chapter considers how such an architecture could be built using contemporary engineering techniques, such as knowledge engineering and symbolic reasoning systems. However, consideration of the philosophical literature on ethical theories generates engineering challenges that have to be overcome to make a computer moral reasoner viable. These difficulties include the context sensitivity of the system and temporal limitations on search—problems specific to artificial intelligence—but also difficulties that are direct consequences of particular philosophical theories. Cooperation between engineers and philosophers may be the best way to deal with those difficulties.

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Similarity-Based Reasoning, Raven's Matrices, and General Intelligence

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/218 ◽

2018 ◽

Author(s):

Can Serif Mekik ◽

Ron Sun ◽

David Yun Dai

Keyword(s):

Analogical Reasoning ◽

Human Subjects ◽

Visual Stimuli ◽

Human Intelligence ◽

General Intelligence ◽

Structure Mapping ◽

Computational System ◽

Reasoning Systems ◽

Feature Based ◽

Raven’S Matrices

This paper presents a model tackling a variant of the Raven's Matrices family of human intelligence tests along with computational experiments. Raven's Matrices are thought to challenge human subjects' ability to generalize knowledge and deal with novel situations. We investigate how a generic ability to quickly and accurately generalize knowledge can be succinctly captured by a computational system. This work is distinct from other prominent attempts to deal with the task in terms of adopting a generalized similarity-based approach. Raven's Matrices appear to primarily require similarity-based or analogical reasoning over a set of varied visual stimuli. The similarity-based approach eliminates the need for structure mapping as emphasized in many existing analogical reasoning systems. Instead, it relies on feature-based processing with both relational and non-relational features. Preliminary experimental results suggest that our approach performs comparably to existing symbolic analogy-based models.

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