This paper describes the development of the Individual Reporting Compliance Model (IRCM), an agent-based model for simulating tax reporting compliance in a community of 85,000 U.S. taxpayers. Design features include detailed tax return characteristics, taxpayer learning, social networks, and tax agency enforcement measures. The taxpayer's compliance reporting decision is modeled as a partially ...

We propose a new approach to the problem of searching a space of stochastic controllers for a Markov decision process (MDP) or a partially observable Markov decision process (POMDP). Following several other authors, our approach is based on searching in parameterized families of policies (for example, via gradient descent) to optimize solution quality. However, rather than trying to estimate th...

The paper describes a method for learning representations of partially observable Markov decision processes in the form of temporal probabilistic networks, which can subsequently be used by robotic agents for action planning and policy determination. A solution is provided to the problem of enforcing stationarity of the learned Markov model. Several preliminary experiments are described that co...

By designing socially intelligent robots that can more effectively communicate and interact with us, we can increase their capacity to function as collaborative partners. Our research goal is to develop robots capable of engaging in nonverbal communication, which has been argued to be at the core of social intelligence. We take a human-centric approach that closely aligns with how people are th...

In this paper we consider an optimal control problem for partially observable Markov decision processes with finite states, signals and actions OVE,r an infinite horizon. It is shown that there are €optimal piecewise·linear value functions and piecl~wise-constant policies which are simple. Simple means that there are only finitely many pieces, each of which is defined on a convex polyhedral set...

This paper deals with three major types of convergence of probability measures on metric spaces: weak convergence, setwise convergence, and convergence in total variation. First, it describes and compares necessary and sufficient conditions for these types of convergence, some of which are well-known, in terms of convergence of probabilities of open and closed sets and, for the probabilities on...

—Spectrum sensing strategy is key to realize cognitive radio. However, spectrum sensing error would affect the access strategy of secondary users in cognitive networks. This paper addresses the spectrum sensing strategy under imperfect spectrum sensing, and proposes opportunistic spectrum access strategies for the imperfect spectrum sensing and fading channels respectively. By setting the opti...

We consider the problem of active feature acquisition (AFA), where the selection of a new feature is conditional on the instantiations of previously selected features. The problem is formulated as a partially observable Markov decision process (POMDP). We present a method to construct an approximate POMDP for the AFA problem and discuss its accuracy. We propose a non-stationary policy to improv...

Breast cancer is a common and deadly disease, but it often curable when diagnosed early. While most countries have large-scale screening programs, there no consensus on single globally accepted guideline for breast screening. The complex nature of the disease; limited availability methods such as mammography, magnetic resonance imaging (MRI), ultrasound; public health policies all factor into d...