Stanford University

AAAI Conference 1999 Conference Paper

A Knowledge-Based Approach to Organizing Retrieved Documents

• Wanda Pratt
• Irvine; Marti A. Hearst
• University of California
• Berkeley; Lawrence M. Fagan
• Stanford University

When people use computer-based tools to find answers to general questions, they often are faced with a daunting list of search results or “hits” returned by the search engine. Many search tools address this problem by helping users to make their searches more specific. However, when dozens or hundreds of documents are relevant to their question, users need tools that help them to explore and to understand their search results, rather than ones that eliminate a portion of those results. In this paper, we present DynaCat, a tool that dynamically categorizes search results into a hierarchical organization by using knowledge of important kinds of queries and a model of the domain terminology. Results from our evaluation show that DynaCat helps users find answers to those important types of questions more quickly and easily than when they use a relevance-ranking system or a clustering system.

AAAI Conference 1999 Conference Paper

Automatic Construction of Semantic Lexicons for Learning Natural Language Interfaces

• Cynthia A. Thompson
• Stanford University
• Raymond J. Mooney
• University of Texas

This paper describes a system, Wolfie (WOrd Learning From Interpreted Examples), that acquires a semantic lexicon from a corpus of sentences paired with semantic representations. The lexicon learned consists of words paired with meaning representations. Wolfie is part of an integrated system that learns to parse novel sentences into semantic representations, such as logical database queries. Experimental results are presented demonstrating Wolfie’s ability to learn useful lexicons for a database interface in four different natural languages. The lexicons learned by Wolfie are compared to those acquired by a similar system developed by Siskind (1996).

AAAI Conference 1999 Short Paper

Elaboration Tolerance of Logical Theories

• Eyal Amir
• Stanford University

We consider the development and modification of logical theories (e.g., commonsense theories). During development of such knowledge bases (KBs) a knowledge engineer makes some design and modeling choices. These decisions may later force the KB to undergo some redesign and rewriting when new knowledge needs to be integrated. We then say that the KB lacks Elaboration Tolerance. McCarthy illustrated this problem using example elaborations for the toy problem of the Missionaries and Cannibals.

AAAI Conference 1999 Conference Paper

Exploiting the Architecture of Dynamic Systems

• Xavier Boyen
• Daphne Koller
• Stanford University

Consider the problem of monitoring the state of a complex dynamic system, and predicting its future evolution. Exact algorithms for this task typically maintain a belief state, or distribution over the states at some point in time. Unfortunately, these algorithms fail when applied to complex processes such as those represented as dynamic Bayesian networks (DBNs), as the representation of the belief state grows exponentially with the size of the process. In (Boyen & Koller 1998), we recently proposed an efficient approximate tracking algorithm that maintains an approximate belief state that has a compact representation as a set of independent factors. Its performance depends on the error introduced by approximating a belief state of this process by a factored one. We informally argued that this error is low if the interaction between variables in the processes is “weak”. In this paper, we give formal information-theoretic definitions for notions such as weak interaction and sparse interaction of processes. We use these notions to analyze the conditions under which the error induced by this type of approximation is small. We demonstrate several cases where our results formally support intuitions about strength of interaction.

AAAI Conference 1999 Short Paper

Learning Design Guidelines by Theory Refinement

• Jacob Eisenstein
• Stanford University

We add adaptation to an existing piece of automatic design software: the TIMM module of the MOBI-D user- interface design environment. MOBI-D maintains explicit, formal representations of the abstract and concrete sides of the interface. TIMM automates the mappings between the abstract domain objects and concrete presentation elements, using a decision tree.

AAAI Conference 1999 Short Paper

Learning of Compositional Hierarchies by Data-Driven Chunking

• Karl Pfleger
• Stanford University

Compositional hierarchies (CHs), layered structures of part-of relationships, underlie many forms of data, and rep-resentations involving these structures lie at the heart of much of AI. Despite this importance, methods for learning CHs from data are scarce. We present an unsupervised technique for learning CHs by an on-line, bottom-up chunking process. At any point, the induced structure can make predictions about new data.

IJCAI Conference 1995 Conference Paper

Agents on Stage: Advancing the State of the Art of AI Barbara

• Hayes-Roth
• Stanford University