We have implemented an application-independent collaboration manager, called Collagen, based on the SharedPlan theory of discourse, and used it to build a software interface agent for a simple air travel application. The software agent provides intelligent, mixed initiative assistance without requiring natural language understanding. A key benefit of the collaboration manager is the automatic construction of an interaction history which is hierarchically structured according to the user's and agent's goals and intentions. To appear in User Modeling and User-Adapted Interaction, Special Issue on Computational Models for Mixed Initiative Interaction. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Ele...

Abstract. Empirical evaluations are needed to determine which users are helped or hindered by user-adapted interaction in user modeling systems. A review of past UMUAI articles reveals insuf¢cient empirical evaluations, but an encouraging upward trend. Rules of thumb for experimental design, useful tests for covariates, and common threats to experimental validity are presented. Reporting standards including effect size and power are proposed. Key words: empirical evaluation, experimental design, covariant variables, e¡ect size, treatment magnitude, power, sensitivity. 1. What Is Empirical Evaluation? Empirical evaluation refers to the appraisal of a theory by observation in experiments. The key to good empirical evaluation is the proper design and execution of the experiments so that the particular factors to be tested can be easily separated from other confounding factors. For example, one may want to test whether a software system with a user model works better than the same system without a user model, test the effect of different levels of user modeling or different user model parameter settings, or test different user interfaces. These factors, which

This paper describes MIMIC, an adaptive mixed initiative spoken dialogue system that provides movie showtime information. MIMIC improves upon previous dialogue systems in two respects. First, it employs initiative-oriented strategy adaptation to automatically adapt response generation strategies based on the cumulative effect of information dynamically extracted from user utterances during the dialogue. Second, MIMIC's dialogue management architecture decouples its initiative module from the goal and response strategy selection processes, providing a general framework for developing spoken dialogue systems with different adaptation behavior.

This work is the first systematic investigation of initiative in human-human tutorial dialogue. We studied initiative management in two dialogue strategies: didactic tutoring and Socratic tutoring.

While researchers have many intuitions about the di#erences between humancomputer and human-human interactions, most of these have not previously been subject to empirical scrutiny. This work presents some initial experiments in this direction, with the ultimate goal being to use what we learn to improve computer dialogue systems. Working with data from the air travel domain, we identified a number of striking differences between the human-human and human-computer interactions.

In this paper, we describe an empirical evaluation of an adaptive mixed initiative spoken dialogue system. We conducted two sets of experiments to evaluate the mixed initiative and automatic adaptation aspects of the system, and analyzed the resulting dialogues along three dimensions: performance factors, discourse features, and initiative distribution. Our results show that 1) both the mixed initiative and automatic adaptation aspects led to better system performance in terms of user satisfaction and dialogue efficiency, and 2) the system's adaptation behavior better matched user expectations, more efficiently resolved dialogue anomalies, and resulted in higher overall dialogue quality.

Abstract. Intelligent dialogue systems usually concentrate on user support at the level of the domain of discourse, following a plan-based approach. Whereas this is appropriate for collaborative planning tasks, the situation in interactive information retrieval systems is quite different: there is no inherent plan–goal hierarchy, and users are known to often opportunistically change their goals and strategies during and through interaction. We need to allow for mixed-initiative retrieval dialogues, where the system evaluates the user, individual dialogue behavior and performs situation-dependent interpretation of user goals, to determine when to take the initiative and to change the control of the dialogue, e.g., to propose (new) problemsolving strategies to the user. In this article, we present the dialogue planning component of a concept-oriented, logic-based retrieval system (MIRACLE). Users are guided through the global stages of the retrieval interaction but may depart, at any time, from this guidance and change the direction of the dialogue. When users submit ambiguous queries or enter unexpected dialogue control acts, abductive reasoning is used to generate interpretations of these user inputs in light of the dialogue history and other internal knowledge sources. Based on these interpretations, the system initiates a short dialogue offering the user suitable options

In this paper we explore student initiative in human tutoring dialogues and how it potentially influences tutor questioning strategies. We describe our annotation scheme for tutor questions and student responses in which student initiative is simply defined as any unsolicited response from the student. We examine what is categorized as student initiative and propose some further distinctions that may be useful for improving mixed-initiative interaction in tutoring systems.

This paper presents some alternate theories for explaining the term "initiative", as it is used in the design of mixed-initiative AI systems. Although there is now active research in the area of mixed initiative interactive systems, there appears to be no true consensus in the field as to what the term "initiative" actually means. In describing different possible approaches to the modeling of initiative, we aim to show the potential importance of each particular theory for the design of mixed initiative systems. The paper concludes by summarizing some of the key points in common to the theories, and by commenting on the inherent difficulties of the exercise, thereby elucidating the limitations which are necessarily encountered in designing such theories as the basis for designing mixed-initiative systems. Keywords: initiative, discourse, goals and plans This paper has not been submitted elsewhere in identical or similar form, nor will it be during the first three months after its subm...

We present an innovative application of discourse processing concepts to educational technology. In our corpus analysis of peer learning dialogues, we found that initiative and initiative shifts are indicative of learning, and of learning-conducive episodes. We are incorporating this finding in KSC-PaL, the peer learning agent we have been developing. KSC-PaL will promote learning by encouraging shifts in task initiative.