Structured Queries for Semistructured Probabilistic Data

We present SPOQL, a structured query language for Semistructured Probabilistic Object (SPO) model [4]. The original querylanguage—SP-Algebra [4], has traditional limitations like terse functional notation and unfamiliarity to application programmers. SPOQL alleviates these problems by providing familiar SQL-like declarative syntax. We show that parsing SPOQL queries is a more involving task than parsing SQL queries. We also present an eagerevaluation algorithm for SPOQL querie

Planning For Success: The Interdisciplinary Approach to Building Bayesian Models

This paper describes a process by which anthropologists, computer scientists, and social welfare case managers collaborated to build a stochastic model of welfare advising in Kentucky. In the process of collaboration,the research team rethought the Bayesian network model of Markov decision processes and designed a new knowledge elicitation format. We expect that this model will have wide applicability in other domains

When Domains Require Modeling Adaptations

The project described in this paper originated with an observation by the AI group at the University of Kentucky, that, individually, stochastic planning and constraint satisfaction are well-studied topics that resulted in efficient software, but stochastic planning in the presence of constraints on the domains and actions is an open area of investigation. We were interested in an advising scenario, and chose the US social welfare system, a.k.a. “Welfare to Work” as our test domain. This required computer scientists to learn more than expected about social science as well as the local welfare system. This paper discusses the discipline specific assumptions we brought to this project, and how they served as impediments to research. We also show how the different perspectives have sparked new ideas in knowledge elicitation

Factored MDP Elicitation and Plan Display

The software suite we will demonstrate at AAAI ’06 was designed around planning with factored Markov decision processes (MDPs). It is a user-friendly suite that facilitates domain elicitation, preference elicitation, planning, and MDP policy display. The demo will concentrate on user interactions for domain experts and those for whom plans are made

Planning for success: The social approach to building Bayesian models

We introduce a new variant of Markov decision processes called MDPs with action results, and a variant of dynamic Bayesian networks called bowties, for modeling the effects of stochastic actions. Bowties grew out of our work on decision-support systems for advisors in the US social welfare system. Bowties, and our elicitation process for them, are designed to elicit dynamic Bayesian network fragments from domain experts who think narratively instead of quantitatively. Our elicitation process has worked well with the welfare case managers and other domain experts, in the sense of capturing consistent and validated models.