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 action...

A class of interval-based temporal languages for uniformly representing and reasoning about actions and plans is presented. Actions are represented by describing what is true while the action itself is occurring, and plans are constructed by temporally relating actions and world states. The temporal languages are members of the family of Description Logics, which are characterized by high expressivity combined with good computational properties. The subsumption problem for a class of temporal Description Logics is investigated and sound and complete decision procedures are given. The basic language TL-F is considered #rst: it is the composition of a temporal logic TL # able to express interval temporal networks # together with the non-temporal logic F # a Feature Description Logic. It is proven that subsumption in this language is an NP-complete problem. Then it is shown how to reason with the more expressive languages TLU-FU and TL-ALCF . The former adds disjunction both at...

Terminological systems, such as KL-ONE and K-Rep, are widely used in AI to represent and reason with concept descriptions. They compute subsumption relations between concepts and automatically classify concepts into a taxonomy. Each concept in the taxonomy describes a set of possible instances which are a superset of those described by its descendants. One limitation of current systems is their inability to handle complex compositions of concepts, such as constraint networks where each node is described by an associated concept. For example, plans are often represented (in part) as collections of actions related by a rich variety of temporal constraints. The T-REX system integrates terminological reasoning with constraint network reasoning to classify such plans, producing a "terminological" plan library. T-REX also introduces a new view of plan recognition as a process which dynamically partitions the plan library by modalities, e.g., necessary, possible and impo...

A formal language for representing and reasoning about time and action is presented. We employ an action representation in the style of Allen, where an action is represented by describing the time course of events while the action occurs. In this sense, an action is defined by means of temporal constraints on the world states, which pertain to the action itself, and on other more elementary actions occurring over time. A distinction between action types and individual actions is supported by the formalism. Plans are seen as complex actions whose properties possibly change with time. The formal representation language used in this paper is a description logic, and it is provided with a well founded syntax, semantics and calculus. Algorithms for the subsumption and recognition tasks -- forming the basis for action management -- are provided. 1 INTRODUCTION The goal of this work is to investigate a formal framework that permits dealing with time, actions and plans in a...

In multiagent domains with adversarial and cooperative team agents, team agents should be adaptive to the current environment and opponent. We introduce an online method to provide the agents with team plans that a "coach" agent generates in response to the specific opponents. The coach agent can observe the agents' behaviors but it has only periodic communication with the rest of the team. The coach uses a Simple Temporal Network to represent team plans as coordinated movements among the multiple agents and the coach searches for an opponent-dependent plan for its teammates. This plan is then communicated to the agents, who execute the plan in a distributed fashion, using information from the plan to maintain consistency among the team members. In order for these plans to be effective and adaptive, models of opponent movement are used in the planning. The coach is then able to quickly select between different models online by using a Bayesian style update on a probability distribution over the models. Planning then uses the model which is found to be the most likely. The system is fully implemented in a simulated robotic soccer environment. In several recent games with completely unknown adversarial teams, the approach demonstrated a visible adaptation to the different teams.

Reasoning about temporal information is an important task in many areas of Artificial Intelligence. In this paper we address the problem of scalability in temporal reasoning by providing a collection of new algorithms for efficiently managing large sets of qualitative temporal relations. We focus on the class of relations forming the Point Algebra (PA-relations) and on a major extension to include binary disjunctions of PA-relations (PA-disjunctions). Such disjunctions add a great deal of expressive power, including the ability to stipulate disjointness of temporal intervals, which is important in planning applications. Our representation of time is based on timegraphs, graphs partitioned into a set of chains on which the search is supported by a metagraph data structure. The approach is an extension of the time representation proposed by Schubert, Taugher and Miller in the context of story comprehension. The algorithms herein enable construction of a timegraph from a given set of PA-r...

Natural language interfaces require dialogue models that allow for robust habitable and efficient interaction. This paper presents such a model for dialogue management for natural language interfaces. The model is based on empirical studies of human computer interaction in various simple service applications. It is shown that for applications belonging to this class the dialogue can be handled using fairly simple means. The interaction can be modeled in a dialogue grammar with information on the functional role of an utterance as conveyed in the linguistic structure. Focusing is handled using dialogue objects recorded in a dialogue tree representing the constituents of the dialogue. The dialogue objects in the dialogue tree can be accessed by the various modules for interpretation generation and background system access. Focused entities are modeled in entities pertaining to objects or sets of objects and related domain concept information; properties of the domain objects. A simple copying principle where a new dialogue object's focal parameters are instantiated with information from the preceding dialogue object accounts for most context dependent utterances. The action to be carried out by the interface is determined on the basis of how the objects and related properties are specified. This in turn depends on information presented in the user utterance context information from the dialogue tree and information in the domain model. The use of dialogue objects facilitates customization to the sublanguage utilized in a specific application. The framework has successfully been applied to various background systems and interaction modalities. In the paper results are presented from the customization of the dialogue manager to three typed interaction applications are prese...

We describe an empirically based approach to the computational management of dialogues. It is based on an explicit theoretically motivated position regarding the status of computational models, where it is claimed that computational models of discourse can only be about computers' processing of language. The dialogue model is based on an extensive analysis of collected dialogues from various application domains. Issues concerning computational tractability has also been decisive for its development. It is concluded that a simple dialogue grammar based model is sufficient for the management of dialogues with natural language interfaces. We also describe the grammar used by the dialogue manager for a Natural Language interface for a database system. Introduction Most, if not all, work on dialogues in presentday computational linguistics do not make explicit to which extent the models and theories developed should be seen as theories about the processing of dialogue by computers or peop...

Goal recognition for dialogue systems needs to be fast, make early predictions, and be portable. We present initial work which shows that using statistical, corpus-based methods to build goal recognizers may be a viable way to meet those needs. Our goal recognizer is trained on data from a plan corpus and then used to determine the agent's most likely goal based on that data. The algorithm is linear in the number of goals, and performs very well in terms of accuracy and early prediction. In addition, it is more easily portable to new domains as does not require a hand-crafted plan library.

this report. Kautz [79] modeled plan recognition logically in a manner that allowed goals and plans to be described at various levels of abstraction. Etzioni et al. [94, 95, 92, 93] developed a version space algorithm for plan recognition that is provably sound and polynomial time [94, 93]. Weld et al. developed goal recognition algorithms using inductive logic programming [90] and version-space algebra [89, 168, 88] in the context of programming by demonstration