Control, Learning and Adaptation in Information-Constrained, Adversarial Environments
Abstract
We propose to develop a theoretical and algorithmic foundation that will help create autonomous robotic agents capable of executing patrol missions in urban environments, possibly in mixed teams of a set of autonomous robotic agents with heterogeneous sensing, perception, computation and actuation capabilities and a smaller number of soldiers (possibly in a supervisory role). To this end, we will formalize a range of problems some of which are considered for the first time in the proposed effort-in the context of partial-information, stochastic games. While partial-information, stochastic games provide a highly expressive modeling language, synthesis of strategies in such games subject to temporal and logical constraints in their general form is known to be algorithmically impractical. Therefore, we plan to establish trade-offs between the expressivity of the problems and their algorithmic and computational tractability through a hierarchy of abstractions.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 14, 2023
- Accession Number
- AD1203833
Entities
People
- Abolfazl Hashemi
- Abraham P. Vinod
- Cyrus Neary
- Dhananjay Raju
- Evan S. Crafts
- Franck Djeumou
- Haris Vikalo
- Joost-Pieter Katoen
- Mahsa Ghasemi
- Marnix Suilen
- Melkior Ornik
- Michael Hibbard
- Murat Cubuktepe
- Mustafa O. Karabag
- Nils Jansen
- Sebastian Junges
- Steven A Carr
- Takashi Tanaka
- Ufuk Topcu
- Yigit E. Bayiz
Organizations
- University of Texas at Austin