Partially observable Markov decision process (POMDP) has been generally used to model agent decision processes such as dialogue management. In this paper, possibility of applying POMDP to a voice activity detector (VAD) has been explored. The proposed system first formulates hypotheses about the current noise environment and speech activity. Then, it decides and observes the features that are e...

A key part of effective teaching is adaptively selecting pedagogical activities to maximize long term student learning. In this poster we report on ongoing work to both develop a tutoring strategy that leverages insights from the partially observable Markov decision process (POMDP) framework to improve problem selection relative to state-of-the-art intelligent tutoring systems, and evaluate the...

In this paper, we describe how techniques from reinforcement learning might be used to approach the problem of acting under uncertainty. We start by introducing the theory of partially observable Markov decision processes (POMDPs) to describe what we call hidden state problems. After a brief review of other POMDP solution techniques, we motivate reinforcement learning by considering an agent wi...

Given the problem of planning actions for situations with uncertainty about the action outcomes, Markov models can eeectively model this uncertainty and ooer optimal actions. When the information about the world state is itself uncertain, partially observable Markov models are an appropriate extension to the basic Markov model. However , nding optimal actions for partially observable Markov mod...

In this paper we discuss how communication can be used advantageously for cooperative navigation in sparse environments. Specifically, we analyze the tradeoff between the cost of communication cost and the efficient completion of the navigation task. We make use of a partially observable Markov decision process (POMDP) to model the navigation task, since this model allows to explicitly consider...

Behavioral tasks are often used to study the different memory systems present in humans and animals. Such tasks are usually designed to isolate and measure some aspect of a single memory system. However, it is not necessarily clear that any given task actually does isolate a system or that the strategy used by a subject in the experiment is the one desired by the experimenter. We have previousl...

In this paper we consider the problem of representing and reasoning about systems, especially probabilistic systems, with hidden state. We consider transition systems where the state is not completely visible to an outside observer. Instead, there are observables that partly identify the state. We show that one can interchange the notions of state and observation and obtain what we call a dual ...

As general purpose robots become more capable, pre-programming of all tasks at the factory will become less practical. We would like for non-technical human owners to be able to communicate, through interaction with their robot, the details of a new task; we call this interaction “task communication”. During task communication the robot must infer the details of the task from unstructured human...

In this paper, we describe the partially observable Markov decision process (pomdp) approach to nding optimal or near-optimal control strategies for partially observable stochastic environments, given a complete model of the environment. The pomdp approach was originally developed in the operations research community and provides a formal basis for planning problems that have been of interest t...

Rheumatoid arthritis is characterized by synovial joint infiltration of activated CD4(+) T cells and MHC class II(+) APC, and is linked to specific HLA-DR alleles. Candidate autoantigens in synovial fluid and cartilage include type II collagen (CII) and cartilage gp39 (HCgp39). Using preparations of native Ag and T cells derived from Ag-immunized DR4-transgenic mice, we determined that human ex...