Computers have already eclipsed the level of human play in competitive Scrabble, but there remains room for improvement. In particular, there is much to be gained by incorporating information about the opponent’s tiles into the decision-making process. In this work, we quantify the value of knowing what letters the opponent has. We use observations from previous plays to predict what tiles our ...

In ci componioiz paper [l] we have developed a frame,work fo,r rating 01‘ comparing navigation packages. For a giuen enuir.onment a navigation package consists of (I motion planner and a sensor to be used during nauigation. The cibility to rate or measure a navigation package is aniportant in order to address issues like sensor custom%zution for an environment and choice of a m.otion plarumr in...

We consider a special case of partially observable Markov decision processes that arises when state information is perfect but arrives with a delay. We rst formulate the decision process in its standard form and derive the Bellman equation that corresponds to it. We then introduce a second decision process that has a much simpler Bellman equation than the rst, and is therefore, in general, much...

Decision theoretic planning in ai bymeans of solving Partially ObservableMarkov decision processes (pomdps) has been shown to be both powerful and versatile. However, such approaches are computationally hard and, from a design stance, are not necessarily intuitive for conceptualising many problems. We propose a novel method for solving pomdps, which provides a designer with a more intuitive mea...

163

A recent insight in the field of decentralized partially observable Markov decision processes (Dec-POMDPs) is that it is possible to convert a Dec-POMDP to a non-observable MDP, which is a special case of POMDP. This technical report provides an overview of this reduction and pointers to related literature.

The paper considers a guiding task in which a robot has to guide a person towards a destination. A robust operation requires to consider uncertain models on the person motion and intentions, as well as noise and occlusions in the sensors employed for the task. Partially Observable Markov Decision Processes (POMDPs) are used to model the task. The paper describes an enhancement on online POMDP s...

How does the brain combine prior knowledge with sensory evidence when making decisions under uncertainty? Two competing descriptive models have been proposed based on experimental data. The first posits an additive offset to a decision variable, implying a static effect of the prior. However, this model is inconsistent with recent data from a motion discrimination task involving temporal integr...

Plan recognition is the problem of inferring the goals and plans of an agent from partial observations of her behavior. Recently, it has been shown that the problem can be formulated and solved using planners, reducing plan recognition to plan generation. In this work, we extend this model-based approach to plan recognition to the POMDP setting, where actions are stochastic and states are parti...