*First Call for papers : Interactions between Analogical Reasoning and MachineLearning workshop at IJCAI (IARML@IJCAI) August 19-23, 2023, Macao. *
Analogical reasoning is a remarkable human capability used to solve hard reasoning tasks. It consists in transferring knowledge from a source domain to a different, but somewhat similar, target domain by relying simultaneously on similarities and differences. Analogies have preoccupied humanity at least since antiquity (cf the works of Aristotle, Theon of Smyrna, among others) and have been in more recent years characterized as being ``at the core of cognition'' (Hofstadter 2001) showing that they permeate almost every aspect of cognition (Hofstadter and Sanders, 2013). Analogies have been tackled from various angles. Traditionally, analogical proportions, i.e., statements of the form ``A is to B as C is to D'', are the basis of analogical inference. They contributed to case-based reasoning and to multiple machine learning tasks such as classification, decision making and machine translation with competitive results. Also, analogical extrapolation can support dataset augmentation (analogical extension) for model learning, especially in environments with few labeled examples. Other approaches include the Structure Mapping approach of Dedre Gentner that is based on logical descriptions (in the form of predicate-argument structures) of two domains: the more relational similarity one has between the two domains, the more analogous they can be considered. According to Hofstadter and the Fluid Analogies Research Group, analogy making is intimately related with abstraction and the search of a ``common essence'', which can lead to deep understanding of any concept or situation. Recent neural techniques, such as representation learning, enabled efficient approaches to detecting and solving analogies in domains where symbolic approaches had shown their limits. Transformer architectures trained using vast amounts of data have given us Large Language Models (LLMs) such as Chat-GPT, seem to exhibit human-like conversational and analogy making capacities (Webb et al. 2022). However, better evaluation metrics are needed in order to measure elusive concepts such as intelligence and understanding (Mitchel 2023). More than ever we need to understand the role that analogies, abstraction and similarities between concepts play in language and cognition. The purpose of this workshop is to bring together AI researchers at the crossroads of machine learning, natural language processing, knowledge representation and reasoning, who are interested in the various applications of analogical reasoning in machine learning or, conversely, of machine learning techniques to improve analogical reasoning. *URL: https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fiarml2023-ijcai.loria.fr%2F&data=05%7C01%7Cuai%40engr.orst.edu%7C1b9b78d3f8804eab59be08db3051188a%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638156897084147963%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=Xl5i%2BuB63gPKfrZNdNKPDsuJ8DEZ%2BeZ9si5HRzpuCmo%3D&reserved=0 <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fiarml2023-ijcai.loria.fr%2F&data=05%7C01%7Cuai%40engr.orst.edu%7C1b9b78d3f8804eab59be08db3051188a%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638156897084147963%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=Xl5i%2BuB63gPKfrZNdNKPDsuJ8DEZ%2BeZ9si5HRzpuCmo%3D&reserved=0>* *Topics:* *Machine learning for analogical reasoning: * – Representation learning; – Advanced similarity measures; – Transfer learning; – Neuro-symbolic models for analogical inference. *Analogical reasoning for machine learning :* – Classification using analogical reasoning; – Recommendation using analogical reasoning; – Case-Based Reasoning; – Creativity and data augmentation. *Analogies in Large Language Models (LLMs) :* – Probing LLMs for analogies; – Evaluating capacities of LLMs for analogies; – Creativity in language through analogies; – Analogies for science creativity. *Applications :* – Analogical reasoning in visual domains; –Analogical reasoning in Natural Language Processing; – Analogical reasoning in healthcare; – Analogie in software engineering. ------------------------------ Paper format: Submitted papers must be formatted according to IJCAI instructions <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fijcai-23.org%2Fcall-for-papers%2F&data=05%7C01%7Cuai%40engr.orst.edu%7C1b9b78d3f8804eab59be08db3051188a%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638156897084147963%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=vkGv7C9fg5r5d4ZXpistnmN%2BmV4vqzfeswHMr9F8k%2FI%3D&reserved=0>. Submissions can constitute original (unpublished) work, work in progress, or mature work that has already been published at other research venues in the form of a survey paper. Previously published work may also be in the form of a position paper that overviews and cites a body of work. However, multiple submissions of the same paper to more IJCAI workshops are forbidden. Review process: All papers will be thoroughly reviewed. Overlength papers will be rejected without review. The reviewing process will be double-blind. Proceedings All papers will appear in the pre-proceedings made available in the workshop webpage. Original contributions will be published in CEUR WS Proceedings. There will also be post-proceedings in Annals of Mathematics and Artificial Intelligence, Springer. ------------------------------ Important dates - May 1, 2023: Paper Due Date - June 5, 2023: Paper Notification - June 30, 2023: Camera-ready - August 19-21, 2023: Workshop IARML@IJCAI 2023 *Organizing committee: * - Miguel Couceiro, University of Lorraine, Loria, France - Stergos Afantenos, University of Toulouse, IRIT, France - Pierre-Alexandre Murena, University of Helsinki, Finland.
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