Abstract
One of the objectives of artificial intelligence is to build decision-support models for systems that evolve over time and include several types of uncertainty. Dynamic limited-memory influence diagrams (DLIMIDs) are a new type of model proposed recently for this kind of problems. DLIMIDs are similar to other models in assuming a multi-stage process that satisfies the Markov property, i.e., that the future is independent of the past given the current state. The main difference with those models is the restriction of limited memory, which means that the decision maker must make a choice based only on recent observations, but this limitation can be circumvented by the use of memory variables. We present several algorithms for evaluating DLIMIDs, show a real-world model for a medical problem, and compare DLIMIDs with related formalisms, such as LIMIDs, dynamic influence diagrams, and POMDPs.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
