Lawrence Birnbaum

The organization of expert systems, a tutorial☆

Abstract This is a tutorial about the organization of expert problem-solving programs. We begin with a restricted class of problems that admits a very simple organization. To make this organization feasible it is required that the input data be static and reliable and that the solution space be small enough to search exhaustively. These assumptions are then relaxed, one at a time, in case study of ten more sophisticated organizational prescriptions. The first cases give techniques for dealing with unreliable data and time-varying data. Other cases show techniques for creating and reasoning with abstract solution spaces and using multiple lines of reasoning. The prescriptions are compared for their coverage and illustrated by examples from recent expert systems.

Information access in context

Our central claim is that user interactions with productivity applications (e.g. word processors, Web browsers, etc.) provide rich contextual information that can be leveraged to support just-in-time access to task-relevant information. As evidence for our claim, we present Watson, a system which gathers contextual information in the form of the text of the document the user is manipulating, in order to proactively retrieve documents from distributed information repositories related to task at hand, as well as process explicit requests in the context of this task. We close by describing the results of several experiments with Watson, which show it consistently provides useful information to its users. The experiments also suggest that, contrary to the assumptions of many system designers, similar documents are not necessarily useful documents in the context of a particular task.

Looking for trouble: Using causal semantics to direct focus of attention

Vision should provide an explanation of the scene in terms of a causal semantics. The authors propose a characterization of what constitutes visual understanding. The cornerstone of the proposal is that visual understanding is fundamentally a matter of developing a causal explanation of the scene, i.e., of determining the causal significance of the elements in a scene, and the causal relationships among those elements. Simple, naive physical knowledge is used as the basis of a vertically integrated vision system that explains arbitrarily complex stacked block structures. The semantics provides a basis for controlling the application of visual attention, and forms a framework for the explanation that is generated. It is shown that the program sequentially explores scenes of complex blocks structures, identifies functional substructures such as arches and cantilevers, and develops an explanation of why the whole construction stands and the role of each block in its stability.<<ETX>>

Opportunistic Planning and Freudian Slips

Freud’s study of the psychology of errors (see, e. g., Freud, 1935), including notably slips of the tongue, led him to the conclusion that many such errors are not merely the result of random malfunctions in mental processing, but rather are meaningful psychological acts. That is, they are intentional actions in every sense of the word, reflecting and indeed carrying out the goals, whether conscious or not, of the person who commits them. In particular, Freud argued, such errors stem from attempts to carry out suppressed intentions, intentions that have been formed but then in some sense withdrawn because they conflict with other, more powerful intentions.

Measuring Semantic Similarity between Named Entities by Searching the Web Directory

The importance of named entities in information retrieval and knowledge management has recently brought interest in characterizing semantic relationships between entities. In this paper, we propose a method for measuring semantic similarity, an important type of semantic relationship, between entities. The method is based on Google Directory, a search interface to the Open Directory Project. Via the search engine, we can locate the web pages relevant to an entity and automatically create a profile of the entity according to the directory assignments of its web pages, which capture various features of the entity. Using their profiles, the semantic similarity between entities can be measured in different dimensions. We apply the semantic similarity measurement to two knowledge acquisition tasks: thesaurus construction of entities and fine grained categorization of entities. Our experiments demonstrate that the proposed method works effectively in these two tasks.

Beyond broadcast

The work presented in this paper takes a novel approach to the task of providing information to viewers of broadcast news. Instead of considering the broadcast news as the end product, this work uses it as a starting point to dynamically build an information space for the user to explore. This information space is designed to satisfy the users information needs, by containing more breadth, depth, and points of view than the original broadcast story. The architecture and current implementation are discussed, and preliminary results from the analysis of some its components are presented

The Role of Self-Models in Learning to Plan

We argue that in order to learn to plan effectively, an agent needs an explicit model of its own planning and plan execution processes. Given such a model, the agent can pinpoint the elements of these processes that are responsible for an observed failure to perform as expected, which in turn enables the formulation of a repair designed to ensure that similar failures do not occur in the future. We have constructed simple models of a number of important components of an intentional agent, including threat detection, execution scheduling, and projection, and applied them to learning within the context of competitive games such as chess and checkers.

Integrating Information Resources: A Case Study of Engineering Design Support

The development of successful case-based design aids depends both on the CBR processes themselves and on crucial questions of integrating the CBR system into the larger task context: how to make the CBR component provide information at the right time and in the right form, how to access relevant information from additional information sources to supplement the case library, how to capture information for use downstream and how to unobtrusively acquire new cases. This paper presents a set of design principles and techniques that integrate methods from CBR and information retrieval to address these questions. The paper illustrates their application through a case study of the Stamping Advisor, a tool to support feasibility analysis for stamped metal automotive parts.

The semantic side of decision making.

The research reported in this paper follows the perspective that decision making is a meaningful act that conveys information. Furthermore, the potential meanings associated with decision options may affect the decisions themselves. This idea is examined in the contexts of compensation, donation, and exchange. In general, judgments were relation dependent and meaning dependent. Furthermore, the results show nonmonotonicities and limited substitutability in a pattern that challenges straightforward ways of mapping decisions onto a common currency of utility.

Compelling intelligent user interfaces—how much AI?

Efforts to incorporate intelligence into the user interface have been underway for decades, but the commercial impact of this work has not lived up to early expectations, and is not immediately apparent. This situation appears to be changing. However, so far the most interesting intelligent user interfaces (IUIS) have tended to use minimal or simplistic AI. In this panel we consider whether more or less AI is the key to the development of compelling IUIS. The panelists will present examples of compelling IUIS that use a selection of AI techniques, mostly simple, but some complex. Each panelist will then comment on the merits of different kinds and quantities of AI in the development of pragmatic interface technology.