Searching for serendipitous analogies

Artificial Intelligence and Cognitive Science, 2002

RADAR is a model of analogy retrieval that employs the principle of systematicity as its primary retrieval cue. RADAR was created to address the current bias toward semantics in analogical retrieval models, to the detriment of structural factors. RADAR recalls 100% of structurally identical domains. We describe a technique based on "derived attributes" that captures structural descriptions of the domain's representation rather than domain contents. We detail their use, recall and performance within RADAR through empirical evidence. We contrast RADAR with existing models of analogy retrieval. We also demonstrate that RADAR can retrieve both semantically related and semantically unrelated domains, even without a complete target description, which plagues current models.

APPLIED INFORMATICS-PROCEEDINGS-, 2001

retrieving analogies from presented problem data is an important phase of analogical reasoning, influencing many related cognitive processes. Existing models have focused on semantic similarity, but structural similarity is also a necessary requirement of any analogical comparison. We present a new technique for performing structure based analogy retrieval. This is founded upon derived attributes that explicitly encode elementary structural qualities of a domains representation. Crucially, these attributes are unrelated to the semantic content of the domain information, and encode only its structural qualities. We describe a number of derived attributes and detail the computation of the corresponding attribute values. We examine our models operation, detailing how it retrieves both semantically related and unrelated domains. We also present a comparison of our algorithms performance with existing models, using a structure rich but semantically impoverished domain.

2012

Are we any closer to creating an autonomous model of analogical reasoning that can generate new and creative analogical comparisons? A three-phase model of analogical reasoning is presented that encompasses the phases of retrieval, mapping and inference validation. The model of the retrieval phase maximizes its creativity by focusing on domain topology, combating the semantic locality suffered by other models. The mapping model builds on a standard model of the mapping phase, again making use of domain topology. A novel validation model helps ensure the quality of the inferences that are accepted by the model. We evaluated the ability of our tri-phase model to re-discover several hcreative analogies (Boden, 1992) from a background memory containing many potential source domains. The model successfully re-discovered all creative comparisons, even when given problem descriptions that more accurately reflect the original problem – rather than the standard (post hoc) representation of the analogy. Finally, some remaining challenges for a truly autonomous creative analogy machine are assessed.

Journal of Experimental and Theoretical Artificial Intelligence , 2022

In this paper, we outline a comprehensive approach to composed analogies based on the theory of conceptual spaces. Our algorithmic model understands analogy as a search procedure and builds upon the idea that analogical similarity depends on a conceptual phenomena called 'dimensional salience.' We distinguish between category-based, property-based, event-based, and part-whole analogies, and propose computationally-oriented methods for explicating them in terms of conceptual spaces.

Knowledge Based Systems, 2006

Analogy is an important reasoning process in creative design. It enables the generation of new design artifacts using ideas from semantically distant domains. Candidate selection is a crucial process in the generation of creative analogies. Without a good set of candidate sources, the success of subsequent phases can be compromised. Two main types of selection have been identified: semantics-based retrieval and structure-based retrieval. This paper presents an empirical study on the importance of the analogy retrieval strategy in the domain of software design. We argue that both types of selection are important, but they play different roles in the process.

2004

Abstract In this paper we present a semantic similarity metric that wholly relies on the hierarchical structure of WordNet which makes it amenable as a means of evaluating creativity when considering creative recategorizations of concepts in an Ontology (Veale, 2004). Many creative discoveries are only acknowledged long after their conception due to changes in the evaluation criteria (Bento and Cardoso, 2004), therefore evaluation plays a critical role in creative reasoning systems.

Metaphors based on perceptual similarity are considered to be one of the hallmarks of creativity. We propose here an image-based retrieval system for generating pairs of perceptually similar images. A number of examples of images paired by the system are presented and discussed. Often, these images are conceptually very different, and hence can serve as anchors for perceptual metaphors that create new conceptual similarities.

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.

Memory & Cognition, 2000

Laboratory studies of analogical reasoning have shown that subjects are mostly influenced by superficial similarity in the retrieval of source analogs. However, real-world investigations have demonstrated that people generate analogies based on deep structural features. We conducted three studies to determine why laboratory and real-world studies have yielded different results. In the first two studies, we used a "production paradigm" in which subjects were asked to generate sources for a given target. Results show the majority of analogies generated displayed low levels of superficial similarity with the target problem. Moreover, most analogies were based on complex underlying structures. The third study used a "reception paradigm" methodology. Participants had to retrieve predetermined sources instead of generating their own. In this case, retrieval was largely constrained by surface similarity. We conclude that people can use structural relations when given an appropriate task and that previous research on analogy has underestimated this ability.

arXiv preprint arXiv:1204.2335, 2012

Abstract: Analogy plays an important role in creativity, and is extensively used in science as well as art. In this paper we introduce a technique for the automated generation of cross-domain analogies based on a novel evolutionary algorithm (EA). Unlike existing work in computational analogy-making restricted to creating analogies between two given cases, our approach, for a given case, is capable of creating an analogy along with the novel analogous case itself. Our algorithm is based on the concept of" memes", which are units ...