Digital libraries of images and video are rapidly growing in size and availability. To avoid the expense and limitations of text, there is considerable interest in navigation by perceptual and other automatically extractable attributes. Unfortunately, the relevance of an attribute for a query is not always obvious. Queries which go beyond explicit color, shape, and positional cues must incorporate multiple features in complex ways. This dissertation uses machine learning to automatically select and combine features to satisfy a query, based on positive and negative examples from the user. The learning algorithm does not just learn during the course of one session: it learns continuously, across sessions. The learner improves its learning ability by dynamically modifying its inductive bias, based on experience over multiple sessions. Experiments demonstrate the ability to assist image classification, segmentation, and annotation (labeling of image regions). The common theme of this work...

Officer praised dog for barking at object." Enables Detect Drugs out FK Initiates Retrieval 5 6 Missing Figure 10. Forgetting to fill the tank with gas A=actual intention; E=expectation; Q=question; C=context; I=index; G=goal Tank Out of Gas Tank Full Tank Low Fill Tank Should have filled up with gas when tank low Expectation What Action to Do? KEY: G = goal; I = index; C = context; Q = question; E = expectation; A = actual intention Results At Store connections with related concepts. Other learning goals take multiple arguments. For instance, a knowledge differentiation goal (Cox & Ram, 1995) is a goal to determine a change in a body of knowledge such that two items are separated conceptually. In contrast, a knowledge reconciliation goal (Cox & Ram, 1995) is one that seeks to merge two items that were mistakenly considered separate entities. Both expansion goals and reconciliation goals may include or spawn a knowledge organization goal (Ram, 1993) that seeks to reorganize the existing knowledge so that it is made available to the reasoner at the appropriate time, as well as modify the structure or content of a concept itself. Such reorganization of knowledge affects the conditions under which a particular piece of knowledge is retrieved or the kinds of indexes associated with an item in memory.

Abstract.. This paper presents a novel case-based plan recognition system that interprets observations of plan behavior using a case library of past observations. The system is novel in that it represents a plan as a sequence of actionstate pairs rather than a sequence of actions preceded by some initial state and followed by some final goal state. The system utilizes a unique abstraction scheme to represent indices into the case base. The paper examines and evaluates three different methods for prediction. The first method is prediction without adaptation; the second is predication with adaptation, and the third is prediction with heuristics. We show that the first method is better than a baseline random prediction, that the second method is an improvement over the first, and that the second and the third methods combined are the best overall strategy.

Most conceptual clustering systems rely solely on data to form concepts without supervision; the few that exploit causalities in the background knowledge do so only after the completion of a similarity-based learning phase. In this paper, we describe a multistrategy misconception discovery system, MMD, that utilizes data and theory in a more tightly coupled way. The integration of similarity- and causality-based learning in MMD is shown to be essential for the automatic construction of accurate and meaningful misconceptions that account for errors in novice behavior. 1