35 research outputs found

    A POMDP approach to Affective Dialogue Modeling

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    We propose a novel approach to developing a dialogue model that is able to take into account some aspects of the user's affective state and to act appropriately. Our dialogue model uses a Partially Observable Markov Decision Process approach with observations composed of the observed user's affective state and action. A simple example of route navigation is explained to clarify our approach. The preliminary results showed that: (1) the expected return of the optimal dialogue strategy depends on the correlation between the user's affective state & the user's action and (2) the POMDP dialogue strategy outperforms five other dialogue strategies (the random, three handcrafted and greedy action selection strategies)

    Toward Affective Dialogue Modeling using Partially Observable Markov Decision Processes

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    We propose a novel approach to developing a dialogue model which is able to take into account some aspects of the userā€™s emotional state and acts appropriately. The dialogue model uses a Partially Observable Markov Decision Process approach with observations composed of the observed userā€™s emotional state and action. A simple example of route navigation is explained to clarify our approach and preliminary results & future plans are briefly discussed

    A tractable DDN-POMDP Approach to Affective Dialogue Modeling for General Probabilistic Frame-based Dialogue Systems

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    We propose a new approach to developing a tractable affective dialogue model for general probabilistic frame-based dialogue systems. The dialogue model, based on the Partially Observable Markov Decision Process (POMDP) and the Dynamic Decision Network (DDN) techniques, is composed of two main parts, the slot level dialogue manager and the global dialogue manager. Our implemented dialogue manager prototype can handle hundreds of slots; each slot might have many values. A first evaluation of the slot level dialogue manager (1-slot case) showed that with a 95% confidence level the DDN-POMDP dialogue strategy outperforms three simple handcrafted dialogue strategies when the user's action error is induced by stress

    Toward affective dialogue management using partially observable Markov decision processes

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    Designing and developing affective dialogue systems have recently received much interest from the dialogue research community. A distinctive feature of these systems is affect modeling. Previous work was mainly focused on showing system's emotions to the user in order to achieve the designer's goal such as helping the student to practice nursing tasks or persuading the user to change their dietary behavior. A challenging problem is to infer the user's affective state and to adapt the system's behavior accordingly. This thesis addresses this problem from an engineering perspective using Partially Observable Markov Decision Process (POMDP) techniques and a Rapid Dialogue Prototyping Methodology (RDPM). We argue that the POMDPs are suitable for use in designing affective dialogue management models for three main reasons. First, the POMDP model allows for realistic modeling of the user's affective state, the user's intention, and other (user's) hidden state components by incorporating them into the state space. Second, recent dialogue management research has shown that the POMDP-based dialogue manager is able to cope well with uncertainty that can occur at many levels inside a dialogue system from speech recognition, natural language understanding to dialogue management. Third, the POMDP environment can be used to create a simulated user which is useful for learning and evaluation of competing dialogue strategies. In the first part of this thesis, we first present the RDPM for a quick production of frame-based dialogue models for traditional (i.e., non-affect sensitive) singleapplication dialogue systems. The usability of the RDPM has been validated through the implementation of several prototype dialogue systems. We then present a novel approach to developing interfaces for multi-application systems which are dialogue systems that allow the user to navigate between a large set of applications smoothly and transparently. The work in this part provides an essential infrastructure for implementing our prototype POMDP-based dialogue manager. In the second part, we first describe a factored POMDP approach to affective dialogue management. This approach illustrates that POMDPs are an elegant model for building affective dialogue systems. Further, the POMDP-based dialogue strategy outperforms all other known strategies from the literature when tested with smallscale dialogue problems. However, a well-known drawback of POMDP-based dialogue managers is that computing a near-optimal dialogue policy is extremely computationally expensive. We then propose a tractable hybrid DDN-POMDP method to tackle many of these scalability problems. The central contribution of our method (compared with other POMDP-based dialogue management methods from the literature) is the ability to handle frame-based dialogue problems with hundreds of slots and hundreds of slot values

    Practical Dialogue Manager Development using POMDPs

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    Four Mode Based Dialogue Management with Modified POMDP Model

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    This thesis proposes a method to manage the interaction between the user and the system dynamically, through speech or text input which updates the user goals, select system actions and calculate rewards for each system response at each time-stamp. The main focus is made on the dialog manager, which decides how to continue the dialogue. We have used POMDP technique, as it maintains a belief distribution on the dialogue states based on the observations over the dialogue even in a noisy environment. Four contextual control modes are introduced in dialogue management for decision-making mechanism, and to keep track of machine behaviour for each dialogue state. The result obtained proves that our proposed framework has overcome the limitations of prior POMDP methods, and exactly understands the actual intention of the users within the available time, providing very interactive conversation between the user and the computer

    An Approach for Intention-Driven, Dialogue-Based Web Search

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    Web search engines facilitate the achievement of Web-mediated tasks, including information retrieval, Web page navigation, and online transactions. These tasks often involve goals that pertain to multiple topics, or domains. Current search engines are not suitable for satisfying complex, multi-domain needs due to their lack of interactivity and knowledge. This thesis presents a novel intention-driven, dialogue-based Web search approach that uncovers and combines users\u27 multi-domain goals to provide helpful virtual assistance. The intention discovery procedure uses a hierarchy of Partially Observable Markov Decision Process-based dialogue managers and a backing knowledge base to systematically explore the dialogue\u27s information space, probabilistically refining the perception of user goals. The search approach has been implemented in IDS, a search engine for online gift shopping. A usability study comparing IDS-based searching with Google-based searching found that the IDS-based approach takes significantly less time and effort, and results in higher user confidence in the retrieved results
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