Will Bridewell

AAAI Conference 2012 Conference Paper

Discovering Constraints for Inductive Process Modeling

• Ljupco Todorovski
• Will Bridewell
• Pat Langley

Scientists use two forms of knowledge in the construction of explanatory models: generalized entities and processes that relate them; and constraints that specify acceptable combinations of these components. Previous research on inductive process modeling, which constructs models from knowledge and time-series data, has relied on handcrafted constraints. In this paper, we report an approach to discovering such constraints from a set of models that have been ranked according to their error on observations. Our approach adapts inductive techniques for supervised learning to identify process combinations that characterize accurate models. We evaluate the method’s ability to reconstruct known constraints and to generalize well to other modeling tasks in the same domain. Experiments with synthetic data indicate that the approach can successfully reconstruct known modeling constraints. Another study using natural data suggests that transferring constraints acquired from one modeling scenario to another within the same domain considerably reduces the amount of search for candidate model structures while retaining the most accurate ones.

AILAW Journal 2010 Journal Article

Emerging AI & Law approaches to automating analysis and retrieval of electronically stored information in discovery proceedings

• Kevin D. Ashley
• Will Bridewell

Abstract This article provides an overview of, and thematic justification for, the special issue of the journal of Artificial Intelligence and Law entitled “E-Discovery”. In attempting to define a characteristic “AI & Law” approach to e-discovery, and since a central theme of AI & Law involves computationally modeling legal knowledge, reasoning and decision making, we focus on the theme of representing and reasoning with litigators’ theories or hypotheses about document relevance through a variety of techniques including machine learning. We also identify two emerging techniques for enabling users’ document queries to better express the theories of relevance and connect them to documents: social network analysis and a hypothesis ontology.

AAAI Conference 2010 Conference Paper

Integrated Systems for Inducing Spatio-Temporal Process Models

• Chunki Park
• Will Bridewell
• Pat Langley

Quantitative modeling plays a key role in the natural sciences, and systems that address the task of inductive process modeling can assist researchers in explaining their data. In the past, such systems have been limited to data sets that recorded change over time, but many interesting problems involve both spatial and temporal dynamics. To meet this challenge, we introduce SCISM, an integrated intelligent system which solves the task of inducing process models that account for spatial and temporal variation. We also integrate SCISM with a constraint learning method to reduce computation during induction. Applications to ecological modeling demonstrate that each system fares well on the task, but that the enhanced system does so much faster than the baseline version.

AAAI Conference 2010 Conference Paper

The Induction and Transfer of Declarative Bias

• Will Bridewell
• Ljupco Todorovski

People constantly apply acquired knowledge to new learning tasks, but machines almost never do. Research on transfer learning attempts to address this dissimilarity. Working within this area, we report on a procedure that learns and transfers constraints in the context of inductive process modeling, which we review. After discussing the role of constraints in model induction, we describe the learning method, MISC, and introduce our metrics for assessing the cost and benefit of transferred knowledge. The reported results suggest that cross-domain transfer is beneficial in the scenarios that we investigated, lending further evidence that this strategy is a broadly effective means for increasing the efficiency of learning systems. We conclude by discussing the aspects of inductive process modeling that encourage effective transfer, by reviewing related strategies, and by describing future research plans for constraint induction and transfer learning.

ICML Conference 2005 Conference Paper

Reducing overfitting in process model induction

• Will Bridewell
• Narges Bani Asadi
• Pat Langley
• Ljupco Todorovski