J. William Murdock

SHOP2: an HTN planning system

The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains.

Explanatory Interface in Interactive Design Environments

Explanation is an important issue in building computer-based interactive design environments in which a human designer and a knowledge system may cooperatively solve a design problem. We consider the two related problems of explaining the system’s reasoning and the design generated by the system. In particular, we analyze the content of explanations of design reasoning and design solutions in the domain of physical devices. We describe two complementary languages: task-method-knowledge models for explaining design reasoning, and structure-behavior-function models for explaining device designs. Interactive Kritik is a computer program that uses these representations to visually illustrate the system’s reasoning and the result of a design episode. The explanation of design reasoning in Interactive Kritik is in the context of the evolving design solution, and, similarly, the explanation of the design solution is in the context of the design reasoning.

Leveraging community-built knowledge for type coercion in question answering

Watson, the winner of the Jeopardy! challenge, is a state-of-the-art open-domain Question Answering system that tackles the fundamental issue of answer typing by using a novel type coercion (TyCor) framework, where candidate answers are initially produced without considering type information, and subsequent stages check whether the candidate can be coerced into the expected answer type. In this paper, we provide a high-level overview of the TyCor framework and discuss how it is integrated in Watson, focusing on and evaluating three TyCor components that leverage the community built semi-structured and structured knowledge resources -- DBpedia (in conjunction with the YAGO ontology), Wikipedia Categories and Lists. These resources complement each other well in terms of precision and granularity of type information, and through links to Wikipedia, provide coverage for a large set of instances.

Towards Design Learning Environments - I: Exploring How Devices Work

Knowledge-based support for learning about physical devices is a classical problem in research on intelligent tutoring systems (ITS). The large amount of knowledge engineering needed, however, presents a major difficulty in constructing ITSs for learning how devices work. Many knowledge-based design systems, on the other hand, already contain libraries of device designs and models. This provides an opportunity for reusing the legacy device libraries for supporting the learning of how devices work. We report on an experiment on the computational feasibility of this reuse of device libraries. In particular, we describe how the structure-behavior-function (SBF) device models in an autonomous knowledge-based design system called KRITIK enable device explanation and exploration in an interactive design and learning environment called Interactive Kritik.

Meta-case-based reasoning: self-improvement through self-understanding

The ability to adapt is a key characteristic of intelligence. In this work we investigate model-based reasoning for enabling intelligent software agents to adapt themselves as their functional requirements change incrementally. We examine the use of reflection (an agents knowledge and reasoning about itself) to accomplish adaptation (incremental revision of an agents capabilities). Reflection in this work is enabled by a language called TMKL (Task-Method-Knowledge Language) which supports modelling of an agents composition and teleology. A TMKL model of an agent explicitly represents the tasks the agent addresses, the methods it applies, and the knowledge it uses. These models are used in a reasoning shell called REM (Reflective Evolutionary Mind). REM enables the execution and incremental adaptation of agents that contain TMKL models of themselves.

Meta-Cases: Explaining Case-Based Reasoning

AI research on case-based reasoning has led to the development of many laboratory case-based systems. As we move towards introducing these systems into work environments, explaining the processes of case-based reasoning is becoming an increasingly important issue. In this paper we describe the notion of a meta-case for illustrating, explaining and justifying case-based reasoning. A meta-case contains a trace of the processing in a problem-solving episode, and provides an explanation of the problem-solving decisions and a (partial) justification for the solution. The language for representing the problem-solving trace depends on the model of problem solving. We describe a task-method-knowledge (TMK) model of problem-solving and describe the representation of meta-cases in the TMK language. We illustrate this explanatory scheme with examples from Interactive Kritik, a computer-based design and learning environment presently under development.

WatsonPaths: Scenario-based Question Answering and Inference over Unstructured Information

We present WatsonPaths, a novel system that can answer scenario-based questions. These include medical questions that present a patient summary and ask for the most likely diagnosis or most appropriate treatment. WatsonPaths builds on the IBM Watson question answering system. WatsonPaths breaks down the input scenario into individual pieces of information, asks relevant subquestions of Watson to conclude new information, and represents these results in a graphical model. Probabilistic inference is performed over the graph to conclude the answer. On a set of medical test preparation questions, WatsonPaths shows a significant improvement in accuracy over multiple baselines.

Scalable cleanup of information extraction data using ontologies

The approach of using ontology reasoning to cleanse the output of information extraction tools was first articulated in SemantiClean. A limiting factor in applying this approach has been that ontology reasoning to find inconsistencies does not scale to the size of data produced by information extraction tools. In this paper, we describe techniques to scale inconsistency detection, and illustrate the use of our techniques to produce a consistent subset of a knowledge base with several thousand inconsistencies.

Assessing elaborated hypotheses: an interpretive case-based reasoning approach

Identifying potential terrorist threats is a crucial task, especially in our post 9/11 world. This task is performed by intelligence analysts, who search for threats in the context of an overwhelming amount of data. We describe AHEAD (Analogical Hypothesis Elaborator for Activity Detection), a knowledge-rich post-processor that analyzes automatically-generated hypotheses using an interpretive case-based reasoning methodology to help analysts understand and evaluate the hypotheses. AHEAD first attempts to retrieve a functional model of a process, represented in the Task-Method-Knowledge framework (Stroulia & Goel, 1995; Murdock & Goel, 2001), to identify the context of a given hypothesized activity. If retrieval succeeds, AHEAD then determines how the hypothesis instantiates the process. Finally, AHEAD generates arguments that explain how the evidence justifies and/or contradicts the hypothesis according to this instantiated process. Currently, we have implemented AHEADs case (i.e., model) retrieval step and its user interface for displaying and browsing arguments in a human-readable form. In this paper, we describe AHEAD and detail its first evaluation. We report positive results including improvements in speed, accuracy, and confidence for users analyzing hypotheses about detected threats.

Learning about Constraints by Reflection

A systems constraints characterizes what that system can do. However, a dynamic environment may require that a system alter its constraints. If feedback about a specific situation is available, a system may be able to adapt by reflecting on its own reasoning processes. Such reflection may be guided not only by explicit representation of the systems constraints but also by explicit representation of the functional role that those constraints play in the reasoning process. We present an operational computer program, SIRRINE2 which uses functional models of a system to reason about traits such as system constraints. We further describe an experiment with SIRRINE2 in the domain of meeting scheduling.