How to advance general game playing artificial intelligence by player modelling

Show simple item record

dc.contributor University of Helsinki, Behavioural Sciences en
dc.contributor.author Cowley, Benjamin Ultan
dc.date.accessioned 2018-02-27T07:46:01Z
dc.date.available 2018-02-27T07:46:01Z
dc.date.issued 2016-06-01
dc.identifier.citation Cowley , B U 2016 , ' How to advance general game playing artificial intelligence by player modelling ' , arXiv.org . < https://arxiv.org/abs/1606.00401 > en
dc.identifier.issn 2331-8422
dc.identifier.other PURE: 89825976
dc.identifier.other PURE UUID: 2f168221-2beb-4711-8287-0c78f8865804
dc.identifier.other ArXiv: http://arxiv.org/abs/1606.00401v3
dc.identifier.other ORCID: /0000-0001-8828-2994/work/42135881
dc.identifier.uri http://hdl.handle.net/10138/232953
dc.description.abstract General game playing artificial intelligence has recently seen important advances due to the various techniques known as 'deep learning'. However the advances conceal equally important limitations in their reliance on: massive data sets; fortuitously constructed problems; and absence of any human-level complexity, including other human opponents. On the other hand, deep learning systems which do beat human champions, such as in Go, do not generalise well. The power of deep learning simultaneously exposes its weakness. Given that deep learning is mostly clever reconfigurations of well-established methods, moving beyond the state of art calls for forward-thinking visionary solutions, not just more of the same. I present the argument that general game playing artificial intelligence will require a generalised player model. This is because games are inherently human artefacts which therefore, as a class of problems, contain cases which require a human-style problem solving approach. I relate this argument to the performance of state of art general game playing agents. I then describe a concept for a formal category theoretic basis to a generalised player model. This formal model approach integrates my existing 'Behavlets' method for psychologically-derived player modelling: Cowley, B., Charles, D. (2016). Behavlets: a Method for Practical Player Modelling using Psychology-Based Player Traits and Domain Specific Features. User Modeling and User-Adapted Interaction, 26(2), 257-306. en
dc.format.extent 7
dc.language.iso eng
dc.relation.ispartof arXiv.org
dc.relation.uri https://arxiv.org/abs/1606.00401
dc.rights en
dc.subject cs.HC en
dc.subject cs.AI en
dc.subject 515 Psychology en
dc.title How to advance general game playing artificial intelligence by player modelling en
dc.type Article
dc.description.version Peer reviewed
dc.type.uri info:eu-repo/semantics/other
dc.type.uri info:eu-repo/semantics/publishedVersion
dc.contributor.pbl

Files in this item

Total number of downloads: Loading...

Files Size Format View
1606.00401v3.pdf 142.9Kb PDF View/Open

This item appears in the following Collection(s)

Show simple item record