Surviving the Information Explosion: How People Find Their Electronic Information

We report on a study of how people look for information within email, files, and the Web. When locating a document or searching for a specific answer, people relied on their contextual knowledge of their information target to help them find it, often associating the target with a specific document. They appeared to prefer to use this contextual information as a guide in navigating locally in small steps to the desired document rather than directly jumping to their target. We found this behavior was especially true for people with unstructured information organization. We discuss the implications of our findings for the design of personal information management tools.

Sequence-Seeking and Counter Streams: A Model for Information Processing in the Cortex

This paper presents a model for the general flow in the neocortex. The basic process, called "sequence-seeking," is a search for a sequence of mappings or transformations, linking source and target representations. The search is bi-directional, "bottom-up" as well as "top-down," and it explores in parallel a large numbe rof alternative sequences. This operation is implemented in a structure termed "counter streams," in which multiple sequences are explored along two separate, complementary pathways which seeking to meet. The first part of the paper discusses the general sequence-seeking scheme and a number of related processes, such as the learning of successful sequences, context effects, and the use of "express lines" and partial matches. The second part discusses biological implications of the model in terms of connections within and between cortical areas. The model is compared with existing data, and a number of new predictions are proposed.

A Unified Statistical and Information Theoretic Framework for Multi-modal Image Registration

We formulate and interpret several multi-modal registration methods in the context of a unified statistical and information theoretic framework. A unified interpretation clarifies the implicit assumptions of each method yielding a better understanding of their relative strengths and weaknesses. Additionally, we discuss a generative statistical model from which we derive a novel analysis tool, the "auto-information function", as a means of assessing and exploiting the common spatial dependencies inherent in multi-modal imagery. We analytically derive useful properties of the "auto-information" as well as verify them empirically on multi-modal imagery. Among the useful aspects of the "auto-information function" is that it can be computed from imaging modalities independently and it allows one to decompose the search space of registration problems.

How People Re-find Information When the Web Changes

This paper investigates how people return to information in a dynamic information environment. For example, a person might want to return to Web content via a link encountered earlier on a Web page, only to learn that the link has since been removed. Changes can benefit users by providing new information, but they hinder returning to previously viewed information. The observational study presented here analyzed instances, collected via a Web search, where people expressed difficulty re-finding information because of changes to the information or its environment. A number of interesting observations arose from this analysis, including that the path originally taken to get to the information target appeared important in its re-retrieval, whereas, surprisingly, the temporal aspects of when the information was seen before were not. While people expressed frustration when problems arose, an explanation of why the change had occurred was often sufficient to allay that frustration, even in the absence of a solution. The implications of these observations for systems that support re-finding in dynamic environments are discussed.

Assimilation of New Information by a Natural Language Understanding System

This work describes a program, called TOPLE, which uses a procedural model of the world to understand simple declarative sentences. It accepts sentences in a modified predicate calculus symbolism, and uses plausible reasoning to visualize scenes, resolve ambiguous pronoun and noun phrase references, explain events, and make conditional predications. Because it does plausible deduction, with tentative conclusions, it must contain a formalism for describing its reasons for its conclusions and what the alternatives are. When an inconsistency is detected in its world model, it uses its recorded information to resolve it, one way or another. It uses simulation techniques to make deductions about creatures motivation and behavior, assuming they are goal-directed beings like itself.

Information Processing and Transmission in Cellular Automata

A cellular automaton is an iterative array of very simple identical information processing machines called cells. Each cell can communicate with neighboring cells. At discrete moments of time the cells can change from one state to another as a function of the states of the cell and its neighbors. Thus on a global basis, the collection of cells is characterized by some type of behavior. The goal of this investigation was to determine just how simple the individual cells could be while the global behavior achieved some specified criterion of complexity ??ually the ability to perform a computation or to reproduce some pattern. The chief result described in this thesis is that an array of identical square cells (in two dimensions), each cell of which communicates directly with only its four nearest edge neighbors and each of which can exist in only two states, can perform any computation. This computation proceeds in a straight forward way. A configuration is a specification of the states of all the cells in some area of the iterative array. Another result described in this thesis is the existence of a self-reproducing configuration in an array of four-state cells, a reduction of four states from the previously known eight-state case. The technique of information processing in cellular arrays involves the synthesis of some basic components. Then the desired behaviors are obtained by the interconnection of these components. A chapter on components describes some sets of basic components. Possible applications of the results of this investigation, descriptions of some interesting phenomena (for vanishingly small cells), and suggestions for further study are given later.

Reinforcement Learning by Policy Search

One objective of artificial intelligence is to model the behavior of an intelligent agent interacting with its environment. The environment's transformations can be modeled as a Markov chain, whose state is partially observable to the agent and affected by its actions; such processes are known as partially observable Markov decision processes (POMDPs). While the environment's dynamics are assumed to obey certain rules, the agent does not know them and must learn. In this dissertation we focus on the agent's adaptation as captured by the reinforcement learning framework. This means learning a policy---a mapping of observations into actions---based on feedback from the environment. The learning can be viewed as browsing a set of policies while evaluating them by trial through interaction with the environment. The set of policies is constrained by the architecture of the agent's controller. POMDPs require a controller to have a memory. We investigate controllers with memory, including controllers with external memory, finite state controllers and distributed controllers for multi-agent systems. For these various controllers we work out the details of the algorithms which learn by ascending the gradient of expected cumulative reinforcement. Building on statistical learning theory and experiment design theory, a policy evaluation algorithm is developed for the case of experience re-use. We address the question of sufficient experience for uniform convergence of policy evaluation and obtain sample complexity bounds for various estimators. Finally, we demonstrate the performance of the proposed algorithms on several domains, the most complex of which is simulated adaptive packet routing in a telecommunication network.

Keyword Join: Realizing Keyword Search in P2P-based Database Systems

In this paper, we present a P2P-based database sharing system that provides information sharing capabilities through keyword-based search techniques. Our system requires neither a global schema nor schema mappings between different databases, and our keyword-based search algorithms are robust in the presence of frequent changes in the content and membership of peers. To facilitate data integration, we introduce keyword join operator to combine partial answers containing different keywords into complete answers. We also present an efficient algorithm that optimize the keyword join operations for partial answer integration. Our experimental study on both real and synthetic datasets demonstrates the effectiveness of our algorithms, and the efficiency of the proposed query processing strategies.