dent processes and domain-specific knowledge. Until recently, information extraction has leaned heavily on domain knowledge, which requires either manual engineering or manual tagging of examples (Miller et al. 1998; Soderland 1999; Culotta, McCallum, and Betz 2006). Semisupervised approaches (Riloff and Jones 1999, Agichtein and Gravano 2000, Rosenfeld and Feldman 2007) require only a small am...

Inference Over the Web Stefan Schoenmackers Co-Chairs of the Supervisory Committee: Professor Oren Etzioni Computer Science and Engineering Professor Daniel S. Weld Computer Science and Engineering The World Wide Web contains vast amounts of text written about nearly any topic imaginable. Recent work in Information Extraction has sought to recover the information stated in this text, aggregatin...

We study problems in multiobjective optimization, in which solutions to a combinatorial optimization problem are evaluated with respect to several cost criteria, and we are interested in the trade-off between these objectives (the so-called Pareto curve). We point out that, under very general conditions, there is a polynomially succinct curve that -approximates the Pareto curve, for any > 0. We...

Recent developments have clari ed the process of generating partially ordered, partially speci ed sequences of actions whose execution will achive an agent's goal. This paper summarizes a progression of least commitment planners, starting with one that handles the simple strips representation, and ending with one that manages actions with disjunctive precondition, conditional e ects and univers...

SPRING 2015 65 Agrowing body of research focuses on extracting knowledge from text such as news reports, encyclopedic articles, and scholarly research in specialized domains. Much of this data is freely available on the World Wide Web and harnessing the knowledge contained in millions of web documents remains a problem of particular interest. The scale and diversity of this content pose a formi...

In real world domains, it is often necessary to avoid some undesirable situations, or intermediate states, from occurring during a plan. For example, when planning the operation of a chemical plant, it is necessary to avoid creating unsafe mixtures of chemicals. This paper presents an algorithm for explicitly preventing unsafe situations from occuring during a plan. The algorithm is presented a...

Explanation-Based Learning (EBL) is a widely-used technique for acquiring searchcontrol knowledge. Recently, Prieditis, van Harmelen, and Bundy pointed to the similarity between Partial Evaluation (PE) and EBL. However, EBL utilizes training examples whereas PE does not. It is natural to inquire, therefore, whether PE can be used to acquire searchcontrol knowledge, and if so at what cost? This ...

We de ne the probabilistic planning problem in terms of a probability distribution over initial world states, a boolean combination of propositions representing the goal, a probability threshold, and actions whose e ects depend on the execution-time state of the world and on random chance. Adopting a probabilistic model complicates the de nition of plan success: instead of demanding a plan that...

& Pow-Sang, J. (2004). Factors predicting prostate specific antigen testing among first-degree relatives of prostate cancer patients. Cancer Epidemiology Biomarkers & Prevention, 13, 753–758. Walter, F. M., Emery, J., Braithwaite, D., & Marteau, T. M. (2004). Lay understanding of familial risk of common chronic diseases: A systematic review and synthesis of qualitative research. Annals of Famil...

Intelligent agency requires some ability to predict the future. An agent must ask itself what is presently its best course of action given what it now knows about what the world will be like when it intends to act. This paper presents a system that uses a probabilistic model to reason about the eeects of an agent's proposed actions on a dynamic and uncertain world, computing the probability tha...