A System for Plan Recognition in Discrete and Continuous Domains

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dc.contributor.advisorLesperance, Yves
dc.contributor.authorScheuhammer, Alistair Sterling Benger
dc.date.accessioned2022-08-08T15:54:39Z
dc.date.available2022-08-08T15:54:39Z
dc.date.copyright2022-01-24
dc.date.issued2022-08-08
dc.date.updated2022-08-08T15:54:39Z
dc.degree.disciplineComputer Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractFor my thesis I seek to implement a programming framework which can be used to model and solve plan recognition problems. My primary goal for this system is for it to be able to easily handle continuous probability spaces as well as discrete ones. My framework is based primarily on the probabilistic situation calculus developed by Belle and Levesque, and is an extension of a programming language developed by Levesque called Ergo. The system I have built allows one to specify complex domains and dynamic models at a high-level and is written in a language which is user-friendly and easy to understand. It has strong formal foundations, can be used to compare multiple different plan recognition methods, and makes it easier to perform plan recognition in tandem with other forms of reasoning, such as threat assessment, reasoning about action, and planning to respond to the actions performed by the observed agent.
dc.identifier.urihttp://hdl.handle.net/10315/39643
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectComputer science
dc.subject.keywordsPlan recognition
dc.subject.keywordsGoal recognition
dc.subject.keywordsContinuous
dc.subject.keywordsDiscrete
dc.subject.keywordsErgo4ppr
dc.subject.keywordsErgo
dc.subject.keywordsArtificial intelligence
dc.subject.keywordsAI
dc.subject.keywordsBasic action theory
dc.subject.keywordsSituation calculus
dc.subject.keywordsBayesian inference
dc.subject.keywordsMonte Carlo sampling
dc.subject.keywordsProbability
dc.titleA System for Plan Recognition in Discrete and Continuous Domains
dc.typeElectronic Thesis or Dissertation

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