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External factors are hard to model using a Markovian state in several real-world planning domains. Although planning can be difficult in such domains, it may be possible to exploit long-term dependencies between states of the environment during planning. We introduce weighted state scenarios to model long-term sequences of states, and we use a model based on a Partially Observable Markov Decision Process to reason about scenarios during planning. Experiments show that our model outperforms other methods for decision making in two real-world domains.
Original languageEnglish
Title of host publicationAAAI 2015 Fall Symposium on Sequential Decision Making for Intelligent Agents
PublisherAmerican Association for Artificial Intelligence (AAAI)
Pages93-94
Number of pages2
StatePublished - 2015
EventAAAI 2015 Fall Symposium Sequential Decision Making for Intelligent Agents - Arlington, Virginia, United States

Conference

ConferenceAAAI 2015 Fall Symposium Sequential Decision Making for Intelligent Agents
CountryUnited States
CityArlington, Virginia
Period12/11/1514/11/15
Internet address

    Research areas

  • Markov decision processes, planning under uncertainty, renewable energy, smart grids

ID: 10680325