
Compact Mathematical Programs For DECMDPs With Structured Agent Interactions
To deal with the prohibitive complexity of calculating policies in Decen...
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Hybrid Informationdriven Multiagent Reinforcement Learning
Information theoretic sensor management approaches are an ideal solution...
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InformationTheoretic Abstractions for ResourceConstrained Agents via MixedInteger Linear Programming
In this paper, a mixedinteger linear programming formulation for the pr...
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A Formal Solution to the Grain of Truth Problem
A Bayesian agent acting in a multiagent environment learns to predict t...
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A Survey of Phase Classification Techniques for Characterizing Variable Application Behavior
Adaptable computing is an increasingly important paradigm that specializ...
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Anytime Heuristic for Weighted Matching Through AltruismInspired Behavior
We present a novel anytime heuristic (ALMA), inspired by the human princ...
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Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints
Determination of inspection and maintenance policies for minimizing long...
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ResourceDriven MissionPhasing Techniques for Constrained Agents in Stochastic Environments
Because an agents resources dictate what actions it can possibly take, it should plan which resources it holds over time carefully, considering its inherent limitations (such as power or payload restrictions), the competing needs of other agents for the same resources, and the stochastic nature of the environment. Such agents can, in general, achieve more of their objectives if they can use  and even create  opportunities to change which resources they hold at various times. Driven by resource constraints, the agents could break their overall missions into an optimal series of phases, optimally reconfiguring their resources at each phase, and optimally using their assigned resources in each phase, given their knowledge of the stochastic environment. In this paper, we formally define and analyze this constrained, sequential optimization problem in both the singleagent and multiagent contexts. We present a family of mixed integer linear programming (MILP) formulations of this problem that can optimally create phases (when phases are not predefined) accounting for costs and limitations in phase creation. Because our formulations multaneously also find the optimal allocations of resources at each phase and the optimal policies for using the allocated resources at each phase, they exploit structure across these coupled problems. This allows them to find solutions significantly faster(orders of magnitude faster in larger problems) than alternative solution techniques, as we demonstrate empirically.
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