{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T10:07:33Z","timestamp":1758708453229},"reference-count":9,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","funder":[{"name":"the Grounded Artificial Intelligence Language Acquisition","award":["HR00111990061"],"award-info":[{"award-number":["HR00111990061"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Semantic Computing"],"published-print":{"date-parts":[[2020,9]]},"abstract":"<jats:p> Recent unsupervised learning approaches have explored the feasibility of semantic analysis and interpretation of imagery using Emergent Language (EL) models. As EL requires some form of numerical embedding as input, it remains unclear which type is required in order for the EL to properly capture key semantic concepts associated with a given domain. In this paper, we compare unsupervised and supervised approaches for generating embeddings across two experiments. In Experiment 1, data are produced using a single-agent simulator. In each episode, a goal-driven agent attempts to accomplish a number of tasks in a synthetic cityscape environment which includes houses, banks, theaters and restaurants. In Experiment 2, a comparatively smaller dataset is produced where one or more objects demonstrate various types of physical motion in a 3D simulator environment. We investigate whether EL models generated from embeddings of raw pixel data produce expressions that capture key latent concepts (i.e. an agent\u2019s motivations or physical motion types) in each environment. Our initial experiments show that the supervised learning approaches yield embeddings and EL descriptions that capture meaningful concepts from raw pixel inputs. Alternatively, embeddings from an unsupervised learning approach result in greater ambiguity with respect to latent concepts. <\/jats:p>","DOI":"10.1142\/s1793351x20400140","type":"journal-article","created":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T09:26:41Z","timestamp":1604309201000},"page":"357-373","source":"Crossref","is-referenced-by-count":4,"title":["Emergent Languages from Pretrained Embeddings Characterize Latent Concepts in Dynamic Imagery"],"prefix":"10.1142","volume":"14","author":[{"given":"James R.","family":"Kubricht","sequence":"first","affiliation":[{"name":"Artificial Intelligence, GE Research, 1 Research Circle, Niskayuna, New York 12309, USA"}]},{"given":"Alberto","family":"Santamaria-Pang","sequence":"additional","affiliation":[{"name":"Artificial Intelligence, GE Research, 1 Research Circle, Niskayuna, New York 12309, USA"}]},{"given":"Chinmaya","family":"Devaraj","sequence":"additional","affiliation":[{"name":"Perception and Robotics, University of Maryland, College Park, Maryland 20742, USA"}]},{"given":"Aritra","family":"Chowdhury","sequence":"additional","affiliation":[{"name":"Artificial Intelligence, GE Research, 1 Research Circle, Niskayuna, New York 12309, USA"}]},{"given":"Peter","family":"Tu","sequence":"additional","affiliation":[{"name":"Artificial Intelligence, GE Research, 1 Research Circle, Niskayuna, New York 12309, USA"}]}],"member":"219","published-online":{"date-parts":[[2020,10,29]]},"reference":[{"key":"S1793351X20400140BIB001","first-page":"2149","volume":"30","author":"Havrylov S.","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"S1793351X20400140BIB002","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2018\/6780564","volume":"2018","author":"Akimoto T.","year":"2018","journal-title":"Adv. Hum.-Comput. Interact."},{"key":"S1793351X20400140BIB003","volume-title":"Causality: Models, Reasoning and Inference","author":"Pearl J.","year":"2000"},{"issue":"10","key":"S1793351X20400140BIB004","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1016\/j.tics.2017.06.002","volume":"21","author":"Kubricht J.","year":"2017","journal-title":"Trends Cogn. Sci."},{"issue":"3","key":"S1793351X20400140BIB005","first-page":"551","volume":"37","author":"Day S. B.","year":"2011","journal-title":"J. Exp. Psychol.: Learn. Mem. Cogn."},{"issue":"3","key":"S1793351X20400140BIB006","doi-asserted-by":"crossref","first-page":"230","DOI":"10.3758\/BF03197721","volume":"15","author":"Beveridge M.","year":"1987","journal-title":"Mem. Cogn."},{"key":"S1793351X20400140BIB007","first-page":"326","volume-title":"Proc. IEEE Conf. Computer Vision and Pattern Recognition","author":"Das A.","year":"2017"},{"key":"S1793351X20400140BIB010","volume-title":"The Visualization Toolkit: An Object-oriented Approach to 3D Graphics","author":"Schroeder W. J.","year":"2004"},{"key":"S1793351X20400140BIB011","volume-title":"Proc. IEEE Conf. Computer Vision and Pattern Recognition","author":"Hara K.","year":"2018"}],"container-title":["International Journal of Semantic Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S1793351X20400140","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T09:30:28Z","timestamp":1604309428000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S1793351X20400140"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9]]},"references-count":9,"journal-issue":{"issue":"03","published-print":{"date-parts":[[2020,9]]}},"alternative-id":["10.1142\/S1793351X20400140"],"URL":"https:\/\/doi.org\/10.1142\/s1793351x20400140","relation":{},"ISSN":["1793-351X","1793-7108"],"issn-type":[{"value":"1793-351X","type":"print"},{"value":"1793-7108","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9]]}}}