Automatic optimization of spoken dialog management policies that are robust to environmental noise has long been the goal for both academia and industry. Approaches based on reinforcement learning have been proved to be effective. However, the numerical representation of dialog policy is human-incomprehensible and difficult for dialog system designers to verify or modify, which limits its practical application. In this paper we propose a novel framework for optimizing dialog policies specified in domain language using genetic algorithm. The human-interpretable representation of policy makes the method suitable for practical employment. We present learning algorithms using user simulation and real human-machine dialogs respectively. Empirical experimental results are given to show the effectiveness of the proposed approach.
The subject of automatically optimizing dialog policies is a hot topic, and many data-driven methods have been proposed among which RL-based ones are the most popular. There is some previous work on constraining the behavior of RL-based DM. In @cite_4 Williams proposed to construct a hand-crafted DM and it produces a set of candidate actions for given dialog state, from which the best one is chosen by a POMDP-DM. Lison @cite_2 proposed to use probabilistic rule' in specifying the transition and reward sub-models of the POMDP model. The probabilistic rules are human-readable and less parameterized than conventional probability distribution, thus reducing the free parameters of the POMDP model and allowing the system designers to make use of domain knowledge in designing DM. Our work bears some resemblance to @cite_2 . But we used the dialog policy template to specify a policy directly and utilized GA to train the free parameters.
@cite_4 @cite_2
2047335008 2773498419 1990671169 196536594
A partially observable Markov decision process (POMDP) has been proposed as a dialog model that enables automatic optimization of the dialog policy and provides robustness to speech understanding errors. Various approximations allow such a model to be used for building real-world dialog systems. However, they require a large number of dialogs to train the dialog policy and hence they typically rely on the availability of a user simulator. They also require significant designer effort to hand-craft the policy representation. We investigate the use of Gaussian processes (GPs) in policy modeling to overcome these problems. We show that GP policy optimization can be implemented for a real world POMDP dialog manager, and in particular: 1) we examine different formulations of a GP policy to minimize variability in the learning process; 2) we find that the use of GP increases the learning rate by an order of magnitude thereby allowing learning by direct interaction with human users; and 3) we demonstrate that designer effort can be substantially reduced by basing the policy directly on the full belief space thereby avoiding ad hoc feature space modeling. Overall, the GP approach represents an important step forward towards fully automatic dialog policy optimization in real world systems. Probabilistic graphical models, such as partially observable Markov decision processes (POMDPs), have been used in stochastic spoken dialog systems to handle the inherent uncertainty in speech recognition and language understanding. Such dialog systems suffer from the fact that only a relatively small number of domain variables are allowed in the model, so as to ensure the generation of good-quality dialog policies. At the same time, the non-language perception modalities on robots, such as vision-based facial expression recognition and Lidar-based distance detection, can hardly be integrated into this process. In this paper, we use a probabilistic commonsense reasoner to “guide” our POMDP-based dialog manager, and present a principled, multimodal dialog management (MDM) framework that allows the robot's dialog belief state to be seamlessly updated by both observations of human spoken language, and exogenous events such as the change of human facial expressions. The MDM approach has been implemented and evaluated both in simulation and on a real mobile robot using guidance tasks. HighlightsWe integrate user appraisals in a POMDP-based dialogue manager procedure.We employ additional socially-inspired rewards in a RL setup to guide the learning.A unified framework for speeding up the policy optimisation and user adaptation.We consider a potential-based reward shaping with a sample efficient RL algorithm.Evaluated using both user simulator (information retrieval) and user trials (HRI). This paper investigates some conditions under which polarized user appraisals gathered throughout the course of a vocal interaction between a machine and a human can be integrated in a reinforcement learning-based dialogue manager. More specifically, we discuss how this information can be cast into socially-inspired rewards for speeding up the policy optimisation for both efficient task completion and user adaptation in an online learning setting. For this purpose a potential-based reward shaping method is combined with a sample efficient reinforcement learning algorithm to offer a principled framework to cope with these potentially noisy interim rewards. The proposed scheme will greatly facilitate the system's development by allowing the designer to teach his system through explicit positive negative feedbacks given as hints about task progress, in the early stage of training. At a later stage, the approach will be used as a way to ease the adaptation of the dialogue policy to specific user profiles. Experiments carried out using a state-of-the-art goal-oriented dialogue management framework, the Hidden Information State (HIS), support our claims in two configurations: firstly, with a user simulator in the tourist information domain (and thus simulated appraisals), and secondly, in the context of man-robot dialogue with real user trials. This paper presents a novel algorithm for learning parameters in statistical dialogue systems which are modelled as Partially Observable Markov Decision Processes (POMDPs). The three main components of a POMDP dialogue manager are a dialogue model representing dialogue state information; a policy which selects the system’s responses based on the inferred state; and a reward function which specifies the desired behaviour of the system. Ideally both the model parameters and the policy would be designed to maximise the reward function. However, whilst there are many techniques available for learning the optimal policy, there are no good ways of learning the optimal model parameters that scale to real-world dialogue systems. The Natural Belief-Critic (NBC) algorithm presented in this paper is a policy gradient method which offers a solution to this problem. Based on observed rewards, the algorithm estimates the natural gradient of the expected reward. The resulting gradient is then used to adapt the prior distribution of the dialogue model parameters. The algorithm is evaluated on a spoken dialogue system in the tourist information domain. The experiments show that model parameters estimated to maximise the reward function result in significantly improved performance compared to the baseline handcrafted parameters.