Plan-based dialogue management in a physics tutor

Freedman, Reva

doi:10.3115/974147.974155

Cited by 22 publications

(17 citation statements)

References 12 publications

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“…Atlas is an ITS that uses natural language dialogs to increase opportunities for students to construct their own knowledge [106]. The two main components of Atlas are APE [107], the Atlas Planning Engine and CARMEL [108], the natural language understanding component. APE is responsible for constructing and generating coherent dialogues while CARMEL understands and analyzes student's answers.…”

Section: Hintmentioning

confidence: 99%

Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments

Alkhatlan¹,

Kalita²

2019

IJCA

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This paper provides interested beginners with an updated and detailed introduction to the field of Intelligent Tutoring Systems (ITS). ITSs are computer programs that use artificial intelligence techniques to enhance and personalize automation in teaching. This paper is a literature review that provides the following: First, a review of the history of ITS along with a discussion on the interface between human learning and computer tutors and how effective ITSs are in contemporary education. Second, the traditional architectural components of an ITS and their functions are discussed along with approaches taken by various ITSs. Finally, recent innovative ideas in ITS systems are presented. This paper concludes with some of the author's views regarding future work in the field of intelligent tutoring systems. General Terms

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Section: Hintmentioning

confidence: 99%

Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments

Alkhatlan¹,

Kalita²

2019

IJCA

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“…The most popular current application of such agents is information access typically using voice-based IO over telephones and mobile devices. At the core of these agents are models that represent the structure of the interaction [Rudnicky and Xu 1999;Freedman 2000;Bangalore et al 2006] and perform functions such as belief update [Williams 2007], confidence annotation [Bohus and Rudnicky 2007], timing [Raux and Eskenazi 2009], and action selection [Paek and Horvitz 2000;Bohus et al 2006;Williams and Young 2007].…”

Section: Introductionmentioning

confidence: 99%

Triggering effective social support for online groups

Kumar

Rosé

2014

ACM Trans. Interact. Intell. Syst.

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Conversational agent technology is an emerging paradigm for creating a social environment in online groups that is conducive to effective teamwork. Prior work has demonstrated advantages in terms of learning gains and satisfaction scores when groups learning together online have been supported by conversational agents that employ Balesian social strategies. This prior work raises two important questions that are addressed in this article. The first question is one of generality. Specifically, are the positive effects of the designed support specific to learning contexts? Or are they in evidence in other collaborative task domains as well? We present a study conducted within a collaborative decision-making task where we see that the positive effects of the Balesian social strategies extend to this new context. The second question is whether it is possible to increase the effectiveness of the Balesian social strategies by increasing the context sensitivity with which the social strategies are triggered. To this end, we present technical work that increases the sensitivity of the triggering. Next, we present a user study that demonstrates an improvement in performance of the support agent with the new, more sensitive triggering policy over the baseline approach from prior work.The technical contribution of this article is that we extend prior work where such support agents were modeled using a composition of conversational behaviors integrated within an event-driven framework. Within the present approach, conversation is orchestrated through context-sensitive triggering of the composed behaviors. The core effort involved in applying this approach involves building a set of triggering policies that achieve this orchestration in a time-sensitive and coherent manner. In line with recent developments in data-driven approaches for building dialog systems, we present a novel technique for learning behaviorspecific triggering policies, deploying it as part of our efforts to improve a socially capable conversational tutor agent that supports collaborative learning.

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“…As mentioned in the introduction, a lot of previous work have considered the problem of automatically generating user-computer dialogues by using planning techniques [4], [5], [6], [7], [8]. APE [7] is a recent system that uses a deterministic dialogue planner that handles the uncertainty of user input by replanning. BEETLE [8] uses a 3-tier planning architecture to handle different aspects in dialogue generation, but still a deterministic planner is used.…”

Section: Conclusion and Related Workmentioning

confidence: 99%

“…Automated planning has long been presented as a promising technique for automatically generating more adaptive usercomputer dialogues [4], [5], [6], [7], [8]. The basic principle is to model speech acts as actions executed by an agent and dialogues as plans (i.e., combinations of actions) for achieving some communication goal.…”

Section: Introductionmentioning

confidence: 99%

Automated planning of tutorial dialogues

Rahati

Kabanza

2010

2010 International Conference on Autonomous and Intelligent Systems, AIS 2010

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Abstract-Managing a dialogue between a student and an intelligent tutoring system is a challenging problem for many applications. It has often been argued and demonstrated that adaptive dialogues between a user and a computer can be generated automatically, using automated planning techniques to plan speech acts. To date such plan-based dialogue generation approaches have relied on deterministic planning algorithms. Consequently they can only handle sequential dialogue structures. In this paper we describe a new approach for automatically planning more general tree-like dialogue structures, by using a nondeterministic planner with incomplete knowledge and sensing. Our approach takes into account incomplete information about the user's knowledge by including queries that the computer can ask to the user to gather missing information that is necessary for an effective feedback. We illustrate our system with an application to an intelligent tutoring system for medical diagnosis.

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Plan-based dialogue management in a physics tutor

Cited by 22 publications

References 12 publications

Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments

Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments

Triggering effective social support for online groups

Automated planning of tutorial dialogues

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