{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,4]],"date-time":"2022-04-04T16:57:24Z","timestamp":1649091444759},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,9,15]]},"abstract":"<jats:p>Normative multi-agent research is an alternative viewpoint in the design of adaptive autonomous agent architecture. Norms specify the standards of behaviors such as which actions or states should be achieved or avoided. The concept of norm synthesis is the process of generating useful normative rules. This study proposes a model for normative rule extraction from implicit learning, namely using the Q-learning algorithm, into explicit norm representation by implementing Dynamic Deontics and Hierarchical Knowledge Base (HKB) to synthesize useful normative rules in the form of weighted state-action pairs with deontic modality. OpenAi Gym is used to simulate the agent environment. Our proposed model is able to generate both obligative and prohibitive norms as well as deliberate and execute said norms. Results show the generated norms are best used as prior knowledge to guide agent behavior and performs poorly if not complemented by another agent coordination mechanism. Performance increases when using both obligation and prohibition norms, and in general, norms do speed up optimum policy reachability.<\/jats:p>","DOI":"10.3233\/faia200555","type":"book-chapter","created":{"date-parts":[[2020,9,16]],"date-time":"2020-09-16T23:01:18Z","timestamp":1600297278000},"source":"Crossref","is-referenced-by-count":0,"title":["Normative Rule Extraction from Implicit Learning into Explicit Representation"],"prefix":"10.3233","author":[{"given":"Mohd Rashdan","family":"Abdul Kadir","sequence":"first","affiliation":[{"name":"School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Johor Baharu, Johor, Malaysia"}]},{"given":"Ali","family":"Selamat","sequence":"additional","affiliation":[{"name":"School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Johor Baharu, Johor, Malaysia"},{"name":"Malaysia Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia"},{"name":"Faculty of Informatics and Management, University of Hradec Kralove, Rokitanskeho62, 500 03 Hradec Kralove, Czech Republic"}]},{"given":"Ondrej","family":"Krejcar","sequence":"additional","affiliation":[{"name":"Faculty of Informatics and Management, University of Hradec Kralove, Rokitanskeho62, 500 03 Hradec Kralove, Czech Republic"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Knowledge Innovation Through Intelligent Software Methodologies, Tools and Techniques"],"original-title":[],"link":[{"URL":"http:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA200555","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,9,17]],"date-time":"2020-09-17T13:32:04Z","timestamp":1600349524000},"score":1,"resource":{"primary":{"URL":"http:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA200555"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,15]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia200555","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,15]]}}}