OLIVO-THESIS-2019.pdf (422.96 kB)
Solving Interactive POMDPS in Julia
thesis
posted on 2019-08-06, 00:00 authored by Iacopo OlivoActing optimally in partially observable multi-agent stochastic domains is a growing topic
in the Artificial Intelligence community and several solutions have been proposed. InteractivePOMDPs are one of the most complete solutions but it is highly susceptible to course of
dimensionality and course of history. Several teams are proposing algorithms to overcome these
difficulties.
In this work it is proposed the framework Julia.POMDPs in order to standardize the development and testing of such solving algorithms (Chapter 3). The framework introduces ways to
declare I-POMDP agents and how to define an agent hierarchy. Julia.IPOMDPs takes advantage of Julia.POMDPs to provide solutions to POMDPs. This project also proposes a new way
to solve I-POMDPs by reducing them to POMDPs (Chapter 4) and solving them by means of
Julia.POMDPs. However, the solver could not provide the expected precision due to loss of
information in the conversion. The on-line solver is tested by the redefinition of the multi-agent
tiger game. Tests and results are analyzed in Chapter 5. The tests are used in order to show
the simplicity of defining several different frames and problem setups.
History
Advisor
Gmytrasiewicz, PiotrChair
Gmytrasiewicz, PiotrDepartment
Computer ScienceDegree Grantor
University of Illinois at ChicagoDegree Level
- Masters
Committee Member
Piccolo, Elio Ziebart, BrianSubmitted date
May 2019Issue date
2019-02-28Usage metrics
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