Approximating behavioral equivalence for scaling solutions of I-DIDs

Approximating behavioral equivalence of models using top-k policy paths

Decision making and game play in multiagent settings must often contend with behavioral models of other agents in order to predict their actions. One approach that reduces the complexity of the unconstrained model space is to group models that tend to be behaviorally equivalent. In this paper, we seek to further compress the model space by introducing an approximate measure of behavioral equiva...

Approximating Markovian testing equivalence

Several approaches have been proposed to relax behavioral equivalences for fine-grain models including probabilities and time. All of them face two problems behind the notion of approximation, i.e. the lack of transitivity and the efficiency of the verification algorithm. While the typical equivalence under approximation is bisimulation, we present a relaxation of Markovian testing equivalence ...

Residual Scaling Techniques in Multigrid , I : Equivalence Proof

This paper addresses the residual scaling techniques (coarse-grid-correction optimization techniques) in multigrid methods. We survey recent developments in this area and prove the equivalence of the over-weighted residual technique and the over-correction technique. This leads to the proof of mathematical equivalence of the pre-scaling and post-scaling acceleration techniques. Two theorems hav...

Residual Scaling Techniques in Multigrid, I: Equivalence Proof

Approximating Value Equivalence in Interactive Dynamic Influence Diagrams Using Behavioral Coverage

Interactive dynamic influence diagrams (I-DIDs) provide an explicit way of modeling how a subject agent solves decision making problems in the presence of other agents in a common setting. To optimize its decisions, the subject agent needs to predict the other agents’ behavior, that is generally obtained by solving their candidate models. This becomes extremely difficult since the model space m...