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Automated Generation of Cross-Domain Analogies Via Automated Generation of Cross-Domain Analogies via Evolutionary Computation Atılım Gunes¸Baydin¨ 1,2, Ramon Lopez´ de Mantaras´ 1, Santiago Ontan˜on´ 3 1Artificial Intelligence Research Institute, IIIA - CSIC Campus Universitat Autonoma` de Barcelona, 08193 Bellaterra, Spain 2Departament d’Enginyeria de la Informacio´ i de les Comunicacions Universitat Autonoma` de Barcelona, 08193 Bellaterra, Spain 3Department of Computer Science, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104, USA [email protected], [email protected], [email protected] Abstract In this paper, we present a technique for the automated generation of cross-domain analogies using evolutionary Analogy plays an important role in creativity, and is computation. Existing research on computational analogy extensively used in science as well as art. In this pa- is virtually restricted to the discovery and assessment of per we introduce a technique for the automated gener- analogies between a given pair of base case A and target ation of cross-domain analogies based on a novel evo- lutionary algorithm (EA). Unlike existing work in com- case B (French 2002) (An exception is the Kilaza model by putational analogy-making restricted to creating analo- O’Donoghue (2004)). On the other hand, given a base case gies between two given cases, our approach, for a given A, the approach that we present here is capable of creating case, is capable of creating an analogy along with the a novel analogous case B itself, along with the analogical novel analogous case itself. Our algorithm is based on mapping between A and B. This capability of open-ended the concept of “memes”, which are units of culture, creation of novel analogous cases is, to our knowledge, the or knowledge, undergoing variation and selection un- first of its kind and makes our approach highly relevant from der a fitness measure, and represents evolving pieces a computational creativity perspective. It replicates the psy- of knowledge as semantic networks. Using a fitness chological observation that an analogy is not always simply function based on Gentner’s structure mapping theory “recognized” between an original case and a retrieved analo- of analogies, we demonstrate the feasibility of sponta- neously generating semantic networks that are analo- gous case, but the analogous case can sometimes be created gous to a given base network. together with the analogy (Clement 1988). As the core of our approach, we introduce a novel evo- lutionary algorithm (EA) based on the concept of “meme” Introduction (Dawkins 1989), where the individuals forming the popula- In simplest terms, analogy is the transfer of information tion represent units of culture, or knowledge, that are under- from a known subject (the analogue or base) onto another going variation, transmission, and selection. We represent particular subject (the target), on the basis of similarity. The individuals as simple semantic networks that are directed cognitive process of analogy is considered at the heart of graphs of concepts and binary relations (Sowa 1991). These many defining aspects of human intellectual capacity, in- go through variation by memetic versions of EA crossover cluding problem solving, perception, memory, and creativity and mutation, which we adapt to work on semantic net- (Holyoak and Thagard 1996); and it has been even argued, works, utilizing the commonsense knowledge bases of Con- by Hofstadter (2001), that analogy is “the core of cognition”. ceptNet (Havasi, Speer, and Alonso 2007) and WordNet Analogy-making ability is extensively linked with cre- (Fellbaum 1998). Defining a memetic fitness measure using ative thought (Hofstadter 1995; Holyoak and Thagard 1996; analogical similarity from Gentner’s psychological structure Ward, Smith, and Vaid 2001; Boden 2004) and plays a mapping theory (Gentner and Markman 1997), we demon- fundamental role in discoveries and changes of knowledge strate the feasibility of generating semantic networks that are in arts as well as science, with key examples such as Jo- analogous to a given base network. hannes Kepler’s explanation of the laws of heliocentric In this introductory work, we focus on the evolution of planetary motion with an analogy to light radiating from analogies using a memetic fitness function promoting analo- the Sun1 (Gentner and Markman 1997); or Ernest Ruther- gies. But it is of note that considering different possible fit- ford’s analogy between the atom and the Solar System2 ness measures, the proposed representation and algorithm (Falkenhainer, Forbus, and Gentner 1989). Boden (2004; can serve as a generic tool for the generation of pieces of 2009) classifies analogy as a form of combinational creativ- knowledge with any desired property that is a quantifiable ity, noting that it works by producing unfamiliar combina- function of the represented knowledge. Our algorithm can tions of familiar ideas. also act as a computational model for experimenting with memetic theories of knowledge, such as evolutionary epis- 1Kepler argued, as light can travel undetectably on its way be- temology and cultural selection theory. tween the source and destination, and yet illuminate the destina- tion, so can motive force be undetectable on its way from the Sun After a review of existing research in analogy, evolution, to planet, yet affect planet’s motion. and creativity, the paper introduces details of our algorithm. 2The Rutherford–Bohr model of the atom considers electrons We then present results and discussion of using the fitness to circle the nucleus in orbits like planets around the Sun, with function based on analogical similarity, and conclude with electrostatic forces providing attraction, rather than gravity. future work and potential applications in creativity. International Conference on Computational Creativity 2012 25 Background Within evolutionary computation, the recently maturing Analogy field of memetic algorithms (MA) has experienced increas- ing interest as a method for solving many hard optimization Analogical reasoning has been actively studied from both problems (Moscato, Cotta, and Mendes 2004). The existing cognitive and computational perspectives. The dominant formulation of MA is essentially a hybrid approach, com- school of research in the field, advanced by Gentner (Falken- bining classical EA with local search, where the population- hainer, Forbus, and Gentner 1989; Gentner and Markman based global sampling of EA in each generation is followed 1997), describes analogy as a structural matching, in which by an individual learning step mimicking cultural evolution, elements from a base domain are mapped to (or aligned performed by each candidate solution. For this reason, this with) those in a target domain via structural similarities of approach has been often referred to under different names their relations. This approach named structure mapping the- besides MA, such as “hybrid EA” or “Lamarckian EA”. To ory, with its computational implementation, the Structure date, MA has been successfully applied to a wide variety of Mapping Engine (SME) (Falkenhainer, Forbus, and Gentner problem domains such as NP-hard optimization problems, 1989), has been cited as the most influential work to date on engineering, machine learning, and robotics. the modeling of analogy-making (French 2002). Alternative The potential of an evolutionary approach to creativity has approaches in the field include the coherence based view been noted from cultural and practical viewpoints (Gabora developed by Holyoak and Thagard (Thagard et al. 1990; 1997; Boden 2009). EA techniques have been shown to em- Holyoak and Thagard 1996), in which analogy is consid- ulate creativity in engineering, such as genetic programming ered as a constraint satisfaction problem involving structure, (GP) introduced by Koza (2003) as being capable of “rou- semantic similarity, and purpose; and the view of Hofstadter 4 (1995) of analogy as a kind of high-level perception, where tinely producing inventive and creative results” ; as well one situation is perceived as another one. Veale and Keane as in visual art, design, and music (Romero and Machado (1997) extend the work in analogical reasoning to the more 2008). In psychology, there are studies providing support specific case of metaphors, which describe the understand- to an evolutionary view of creativity, such as the behavioral ing of one kind of thing in terms of another. A highly re- analysis by Simonton (2003) inferring that scientific creativ- lated cognitive theory is the conceptual blending idea de- ity constitutes a form of constrained stochastic behavior. veloped by Fauconnier and Turner (2002), which involves connecting several existing concepts to create new meaning, The Algorithm operating below the level of consciousness as a fundamental Our approach is based on a meme pool comprising individ- mechanism of cognition. An implementation of this idea is uals represented as semantic networks, subject to variation given by Pereira (2007) as a computational model of abstract and selection under a fitness measure. We position our al- thought, creativity, and language. gorithm as a novel memetic algorithm, because (1) it is the According to whether the base and target cases belong to units of culture, or information, that are undergoing varia- the same or different domains, there are two types of anal- tion, transmission, and selection, very close to the original ogy: intra-domain, confined to surface similarities within sense of “memetics” as it
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