In this paper we discuss the modelling of diagnosis and treatment of diseases through the use of Partially Observable Markov Decision Processes, with the possibility of optimizing any criteria that may be quantified. Such model could be used in the making of a tool for aiding physicians with a second opinion.

Decision processes with incomplete state feedback have been traditionally modelled as partially observable Markov decision processes. In this article, we present an alternative formulation based on probabilistic regular languages. The proposed approach generalises the recently reported work on language measure theoretic optimal control for perfectly observable situations and shows that such a f...

We analyze the asymptotic behavior of agents engaged in an infinite horizon partially observable stochastic game as formalized by the interactive POMDP framework. We show that when agents’ initial beliefs satisfy a truth compatibility condition, their behavior converges to a subjective ǫ-equilibrium in a finite time, and subjective equilibrium in the limit. This result is a generalization of a ...

Partially observable planning is one of the most general and useful models for dealing with complex problems. In recent years there have been significant progress on the development of planners for deterministic models that offer strong theoretical guarantees over certain subclasses of tasks. These guarantees however are difficult to establish as they often involve reasoning about features that...

Many decision-making problems can be formulated in the framework of a partially observable Markov decision process (POMDP) [5]. The optimality of decisions relies on the accuracy of the POMDP model as well as the policy found for the model. In many applications the model is unknown and learned empirically based on experience, and building a model is just as difficult as finding the associated p...

We study finite-state controllers (FSCs) for partially observable Markov decision processes (POMDPs). The key insight is that computing (randomized) FSCs on POMDPs is equivalent to synthesis for parametric Markov chains (pMCs). This correspondence enables using parameter synthesis techniques to compute FSCs for POMDPs in a black-box fashion. We investigate how typical restrictions on parameter ...

The degree of confidence in one’s choice or decision is a critical aspect of perceptual decision making. Attempts to quantify a decision maker’s confidence by measuring accuracy in a task have yielded limited success because confidence and accuracy are typically not equal. In this paper, we introduce a Bayesian framework to model confidence in perceptual decision making. We show that this model...

We discuss the problem of comparing the behavioural equivalence of partially observable systems with observations. We examine different types of equivalence relations on states, and show that branching equivalence relations are stronger than linear ones. Finally, we discuss how this hierarchy can be used in duality theory.