Partially Observable Markov Decision Processes (POMDPs) have been applied extensively to planning in environments where knowledge of an underlying process is confounded by unknown factors[3, 4, 7]. By applying the POMDP architecture to a basic recognition task, we introduce a novel pattern recognizer that operates under partially observable conditions. This Single Action Partially Observable Ma...

Partially Observable Markov Decision Processes (POMDPs) have been applied extensively to planning in environments where knowledge of an underlying process is confounded by unknown factors[3, 4, 7]. By applying the POMDP architecture to basic recognition tasks, we introduce a novel pattern recognizer that operates under partially observable conditions. This Single Action Partially Observable Mar...

We describe a new planning technique that efficiently solves probabilistic propositional contingent planning problems by converting them into instances of stochastic satisfiability (SSat) and solving these problems instead. We make fundamental contributions in two areas: the solution of SSat problems and the solution of stochastic planning problems. This is the first work extending the planning...

Partially Observable Markov Decision Processes (POMDPs) is a well-developed framework for sequential decision making under uncertainty and partial information. This paper considers the (inverse) structural estimation of primitives POMDP based upon data in form sequences observables implemented actions. We analyze properties an entropy regularized specify conditions which model identifiable with...

We study the problem of synthesizing a controller that maximizes entropy partially observable Markov decision process (POMDP) subject to constraint on expected total reward. Such minimizes predictability an agent’s trajectories outside observer while guaranteeing completion task expressed by reward function. Focusing finite-state controllers (FSCs) with deterministic memory transitions, we show...

The value 1 problem is a natural decision problem in algorithmic game theory. For partially observable Markov decision processes with reachability objective, this problem is defined as follows: are there strategies that achieve the reachability objective with probability arbitrarily close to 1? This problem was shown undecidable recently. Our contribution is to introduce a class of partially ob...

The value 1 problem is a natural decision problem in algorithmic game theory. For partially observable Markov decision processes with reachability objective, this problem is defined as follows: are there observational strategies that achieve the reachability objective with probability arbitrarily close to 1? This problem was shown undecidable recently. Our contribution is to introduce a class o...

The contribution of the UvA Rescue Team is an attempt to lay a theoretical foundation by describing the planning and coordination problem formally as an POMDP problem, which will allow to apply POMDP-solution methods in this application area. To be able to solve the POMDP problem for large state spaces and long planning histories, our team has chosen for an approximation of the Dec-POMDPs throu...