This file contains bibliographic citations (with abstracts) for some
of my recent papers. Most citations end with the name of a compressed
postscript file. These files are available via anonymous ftp from
ftp.cs.rochester.edu in directory
pub/papers/ai, or (if your browser supports it) by
following the link.
We discuss what constitutes an integrated system in AI, and why AI researchers should be interested in building and studying them. Taking integrated systems to be ones that integrate a variety of components in order to perform some task from start to finish, we believe that such systems (a) allow us to better ground our theoretical work in actual tasks, and (b) provide an opportunity for much-needed evaluation based on task performance. We describe one particular integrated system we have developed that supports spoken-language dialogue to collaboratively solve planning problems. We discuss how the integrated system provides key advantages for helping both our work in natural language dialogue processing and in interactive planning and problem solving, and consider the opportunities such an approach affords for the future.
This document describes the design and implementation of TRAINS-96, a prototype mixed-initiative planning assistant system. The TRAINS-96 system helps a human manager solve routing problems in a simple transportation domain. It interacts with the human using spoken, typed, and graphical input and generates spoken output and graphical map displays. The key to TRAINS-96 is that it treats the interaction with the user as a dialogue in which each participant can do what they do best. The TRAINS-96 system is intended as both a demonstration of the feasibility of realistic mixed-initiative planning and as a platform for future research. This document describes both the design of the system and such features of its use as might be useful for further experimentation. Further references and a comprehensive set of manual pages are also provided.
We have been examining mixed-initiative planning systems in the context of command and control or logistical overview situations. In such environments, the human and the computer must work together in a very tightly coupled way to solve problems that neither alone could manage. In this paper, we describe our implementation of a prototype version of such a system, TRAINS-95, which helps a manager solve routing problems in a simple transportation domain. Interestingly perhaps, traditional planning technology does not play a major role in the system, and in fact it is difficult to see how such components might fit into a mixed-initiative system. We describe some of these issues, and present our agenda for future research into mixed-initiative plan reasoning.
This paper presents an overview of research at Rochester addressing problems in developing large-scale plans in complex worlds. The work can be divided into three general areas. We address representational issues by developing new representations of actions and plans that increase the the expressiveness of plan representations, especially in dealing with external events and interacting overlapping actions. We address efficiency issues by developing a set of temporal reasoning algorithms for the efficient handling of very large-scale temporal databases. And finally, we address the problem of developing plans in the real world by defining a model of mixed-initiative planning using an interactive dialogue-based model of plan management. By viewing the human as an essential part of the planning process, we dramatically change the problems that are important for the ultimate successful application of planning technology.
This paper explores spoken dialogue as a powerful and natural medium for mixed-initiative planning. It both presents our vision of the interactive mixed-initiative planning system of the future, and gives a brief report on our current progress.
This dissertation describes the formal foundations and implementation of a commonsense, mixed-initiative plan reasoning system. By ``plan reasoning'' I mean the complete range of cognitive tasks that people perform with plans including, for example, plan construction (planning), plan recognition, plan evaluation and comparison, and plan repair (replanning), among other things. ``Mixed-initiative'' means that several participants can each make contributions to the plan under development through some form of communication. ``Commonsense'' means that the system represents plans and their constituents at a level that is ``natural'' to us in the sense that they can be described and discussed in language. In addition, the reasoning that the system performs includes those conclusions that we would take to be sanctioned by common sense, including especially those conclusions that are defeasible given additional knowledge or time spent reasoning.The main theses of this dissertation are the following:
These theses are defended by presenting, first, a representation of events and actions based on interval temporal logic and, second, a representation of plans as arguments in a formal system of defeasible reasoning that explicitly constructs arguments. These two aspects of commonsense plan reasoning are combined and implemented in the TRAINS domain plan reasoner, which is also described in detail.
- Any representation of plans sufficient for commonsense plan reasoning must be based on an expressive and natural representation of such underlying phenomena as time, properties, events, and actions.
- For mixed-initiative planning, plans should be viewed as arguments that a certain course of action under certain conditions will achieve certain goals.
The emphasis in this dissertation is on breadth, taking as its data human communicative and plan reasoning abilities and developing formalisms that characterize these abilities and systems that approximate them. I therefore draw on literature from a broad range of disciplines in the development of these ideas, including: philosophy of language, linguistics and AI work on knowledge representation for the representation of events and actions, philosophical logic and AI work on nonmonotonic reasoning for representing defeasible knowledge and reasoning about it, and, of course, AI work on planning and planning recognition itself.
The TRAINS project is an effort to build a conversationally proficient planning assistant. A key part of the project is the construction of the TRAINS system, which provides the research platform for a wide range of issues in natural language understanding, mixed-initiative planning systems, and representing and reasoning about time, actions and events. Four years have now passed since the beginning of the project. Each year we have produced a demonstration system that focused on a dialog that illustrates particular aspects of our research. The commitment to building complete integrated systems is a significant overhead on the research, but we feel it is essential to guarantee that the results constitute real progress in the field. This paper describes the goals of the project, and our experience with the effort so far.
We present a representation of events and action based on interval temporal logic that is significantly more expressive and more natural than most previous AI approaches. The representation is motivated by work in natural language semantics and discourse, temporal logic, and AI planning and plan recognition. The formal basis of the representation is presented in detail, from the axiomatization of time periods to the relationship between actions and events and their effects. The power of the representation is illustrated by applying it to the axiomatization and solution of several standard problems from the AI literature on action and change. An approach to the frame problem based on explanation closure is shown to be both powerful and natural when combined with our representational framework. We also discuss features of the logic that are beyond the scope of many traditional representations, and describe our approach to difficult problems such as external events and simultaneous actions.
The long term goal of the TRAINS project is to develop an intelligent planning assistant that is conversationally proficient in natural language. The TRAINS system helps a user construct and monitor plans about a railroad freight system; their interaction takes place in natural language. The representational needs of a system like TRAINS include representing lexical meaning, dealing with the problem of ambiguity , make use of information about context, and finding a connection between the content of the current utterance and the plan being jointly developed by system and user . The goal of the paper is to describe how TRAINS-93, the latest prototype of TRAINS, deals with these issues.
We consider the problem of representing plans for mixed-initiative planning, where several participants cooperate to develop plans. We claim that in such an environment, a crucial task is plan communication: the ability to suggest aspects of a plan, accept such suggestions from other agents, criticize plans, revise them, etc., in addition to building plans. The complexity of this interaction imposes significant new requirements on the representation of plans. We describe a formal model of plans based on defeasible argument systems that allows us to perform these types of reasoning. The arguments that are produced are explicit objects that can be used to provide a semantics for statements about plans.
This paper describes the TRAINS-93 Conversation System, an implemented system that acts as an intelligent planning assistant and converses with the user in natural language. The architecture of the system is described and particular attention is paid to the interactions between the language understanding and plan reasoning components. We examine how these two tasks constrain and inform each other in an integrated NL-based system.
We describe a general framework for encoding rich domain models and sophisticated plan reasoning capabilities. The approach uses graph-based reasoning to address a wide range of tasks that typically arise in dialogue systems. The graphical plan representation is independent of but connected to the underlying representation of action and time. We describe types of plan recognition that are needed, illustrating these with examples from dialogues collected as part of the TRAINS project. The algorithms for the tasks are presented, and issues in the formalization of the reasoning processes are discussed.
We propose a wide-ranging knowledge representation formalism designed expressly to support many different forms of reasoning about plans. We begin with an event-based language based on the interval temporal logic. The language supports reasoning about action attempts and composite actions, both of which are given axiomatic definitions. We then define a representation for plans viewed as arguments that a certain course of action under certain explicit conditions will achieve certain goals. We can represent both correct and incorrect plans, and reason about why they might or might not fail. An important aspect of this work is the formal characterization of plan reasoning as assumption-based reasoning, to make the non-deductive aspects of plan reasoning explicit. A preliminary implementation of these ideas has already been built as the plan reasoning component of the TRAINS system.