We address the problem of dynamically modeling and adapting to unknown users in resource-scarce domains in the context of interactive spoken dialogue systems. As an example, we show how a system can learn to choose referring expressions to refer to domain entities for users with different levels of domain expertise, and whose domain knowledge is initially unknown to the system. We approach this...

Adaptive social learning is a useful tool for studying distributed decision-making problems over graphs. This paper investigates the effect of combination policies on performance adaptive strategies. Using large-deviation analysis, it first derives bound steady-state error probability and characterizes optimal selection Perron eigenvectors policies. It subsequently studies policy transient beha...

We investigate the behaviour of load-adaptive rerouting policies in the Wardrop model where decisions must be made on the basis of stale information. In this model, an infinite number of agents controls an infinitesimal amount of flow each, thus contributing to a network flow which induces latency. In our dynamic extension of this model, agents are activated in a concurrent and asynchronous fas...

Adaptive Sensing Techniques for Dynamic Target Tracking and Detection with Applications to Synthetic Aperture Radars by Gregory Evan Newstadt Chair: Alfred O. Hero, III This thesis studies adaptive allocation of a limited set of sensing or computational resources in order to maximize some criteria, such as detection probability, estimation accuracy, or throughput, with specific application to i...

Policies are becoming increasingly important in modern computer systems as a mechanism for end users and organisations to exhibit a level of control over software. Policies have long been established as an effective mechanism for enabling appropriate access control over resources, and for enforcing security considerations. However they are now becoming valued as a more general management mechan...

Multi-agent planning problems with constraints on global resource consumption occur in several domains. Existing algorithms for solving Multi-agent Markov Decision Processes can compute policies that meet a resource constraint in expectation, but these policies provide no guarantees on the probability that a resource constraint violation will occur. We derive a method to bound constraint violat...

We show how to learn optimal dialogue policies for a wide range of database search applications, concerning how many database search results to present to the user, and when to present them. We use Reinforcement Learning methods for a wide spectrum of different database simulations, turn penalty conditions, and noise conditions. Our objective is to show that our policy learning framework covers...

Multi-agent planning problems with constraints on global resource consumption occur in several domains. Existing algorithms for solving Multi-agent Markov Decision Processes can compute policies that meet a resource constraint in expectation, but these policies provide no guarantees on the probability that a resource constraint violation will occur. We derive a method to bound constraint violat...

Solving stochastic optimization problems under partial observability, where one needs to adaptively make decisions with uncertain outcomes, is a fundamental but notoriously difficult challenge. In this paper, we introduce the concept of adaptive submodularity, generalizing submodular set functions to adaptive policies. We prove that if a problem satisfies this property, a simple adaptive greedy...

The regional scale has become increasingly important for natural resource management, with funding support focused on regional plans. It is also the scale at which adaptive management an approach to managing natural resources that actively seeks to learn from the implementation of policies and strategies could be expected to work best. This article explores passive and active adaptive managemen...