{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T16:49:11Z","timestamp":1764780551819,"version":"3.46.0"},"reference-count":8,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["AI Matters"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:p>The use of AI to play games has a long history. Some of the earliest examples were deterministic, fully observable games with clearly defined rules and possible game states. AI has proven best in games of this nature. Games such as checkers are considered solvable (Schaeffer et al., 2007) because there is always an optimal move to be made at any given point in the game, which means an AI agent can mathematically learn the best move.<\/jats:p>","DOI":"10.1145\/3774399.3774405","type":"journal-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T16:45:18Z","timestamp":1764780318000},"page":"29-38","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Interpolating Humour \u2014 Can Lines Be Funny?"],"prefix":"10.1145","volume":"11","author":[{"given":"Oscar","family":"De Leon","sequence":"first","affiliation":[{"name":"MacEwan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Isaac","family":"McCracken","sequence":"additional","affiliation":[{"name":"MacEwan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kevin","family":"Ulliac","sequence":"additional","affiliation":[{"name":"MacEwan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Calin","family":"Anton","sequence":"additional","affiliation":[{"name":"MacEwan University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,12,3]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"n19a","volume-title":"Proceedings of the 36th international conference on machine learning (Vol. 97","author":"Gultchin L.","year":"2019","unstructured":"Gultchin, L., Patterson, G., Baym, N., Swinger, N., & Kalai, A. (2019, 09\u201315 Jun). Humor in word embeddings: Cockamamie gobbledegook for nincompoops. In K. Chaudhuri & R. Salakhutdinov (Eds.), Proceedings of the 36th international conference on machine learning (Vol. 97, pp. 2474\u20132483). PMLR. Retrieved from https:\/\/proceedings.mlr.press\/v97\/gultchin19a.html"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","unstructured":"Guo X. Yu H. Li B. Wang H. Xing P. Feng S. ... Miao C. (2022 May). Federated learning for personalized humor recognition. ACM Trans. Intell. Syst. Technol. 13(4). Retrieved from doi: 10.1145\/3511710 10.1145\/3511710","DOI":"10.1145\/3511710"},{"key":"e_1_2_1_3_1","volume-title":"the International conference on learning representations.","author":"Mikolov T.","year":"2013","unstructured":"Mikolov, T., Chen, K., Corrado, G. S., & Dean, J. (2013). Efficient estimation of word representations in vector space. In the International conference on learning representations. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:5959482"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.findings-emnlp.394"},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of the joint workshop on multiword expressions and electronic lexicons (pp. 95\u2013100)","author":"Pickard T.","year":"2020","unstructured":"Pickard, T. (2020, December). Comparing word2vec and GloVe for automatic measurement of MWE compositionality. In S. Markantonatou et al. (Eds.), Proceedings of the joint workshop on multiword expressions and electronic lexicons (pp. 95\u2013100). online: Association for Computational Linguistics. Retrieved from https:\/\/aclanthology.org\/2020.mwe-1.12\/"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1144079"},{"key":"e_1_2_1_7_1","article-title":"03). From frequency to meaning: Vector space models of semantics","volume":"10","author":"Turney P.","year":"2010","unstructured":"Turney, P., & Pantel, P. (2010, 03). From frequency to meaning: Vector space models of semantics. Journal of Artificial Intelligence Research, 37. doi: 10.1613\/ jair.2934","journal-title":"Journal of Artificial Intelligence Research, 37. doi"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1372"}],"container-title":["AI Matters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3774399.3774405","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T16:45:19Z","timestamp":1764780319000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3774399.3774405"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10]]},"references-count":8,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["10.1145\/3774399.3774405"],"URL":"https:\/\/doi.org\/10.1145\/3774399.3774405","relation":{},"ISSN":["2372-3483"],"issn-type":[{"value":"2372-3483","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10]]},"assertion":[{"value":"2025-12-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}