In recent years, with the spread of the household robots, the necessity to enhance the communication capabilities of those robot to people has been increasing. The objective of this study is to build a framework for a dialogue system dealing with multimodal information that a robot observes. We have applied partially observable Markov Decision Process to modeling multimodal interaction between ...

This paper introduces timeline trees, which are partial models of partially observable environments. Timeline trees are given some specific predictions to make and learn a decision tree over history. The main idea of timeline trees is to use temporally abstract features to identify and split on features of key events, spread arbitrarily far apart in the past (whereas previous decision-tree-base...

Deciding how to act in partially observable environments remains an active area of research. Identifying good sequences of decisions is particularly challenging when good control performance requires planning multiple steps into the future in domains with many states. Towards addressing this challenge, we present an online, forward-search algorithm called the Posterior Belief Distribution (PBD)...

We demonstrate an information navigation system for sightseeing domains that has a dialogue interface for discovering user interests for tourist activities. The system discovers interests of a user with focus detection on user utterances, and proactively presents related information to the discovered user interest. A partially observable Markov decision process (POMDP)-based dialogue manager, w...

This paper proposes a new heuristic algorithm suitable for real-time applications using partially observable Markov decision processes (POMDP). The algorithm is based in a reward shaping strategy which includes entropy information in the reward structure of a fully observable Markov decision process (MDP). This strategy, as illustrated by the presented results, exhibits near-optimal performance...

A fundamental problem in cognitive neuroscience is how humans make decisions, act, and behave in relation to other humans. Here we adopt the hypothesis that when we are in an interactive social setting, our brains perform Bayesian inference of the intentions and cooperativeness of others using probabilistic representations. We employ the framework of partially observable Markov decision process...

We present a computational framework for understanding Theory of Mind (ToM): the human capacity for reasoning about agents’ mental states such as beliefs and desires. Our Bayesian model of ToM (or BToM) expresses the predictive model of beliefand desire-dependent action at the heart of ToM as a partially observable Markov decision process (POMDP), and reconstructs an agent’s joint belief state ...

A new policy iteration algorithm for partially observable Markov decision processes is presented that is simpler and more efficient than an earlier policy iteration algorithm of Sondik (1971,1978). The key simplification is representation of a policy as a finite-state controller. This representation makes policy evaluation straightforward. The paper's contribution is to show that the dynamic-pr...

This paper presents an approach to building plans using partially observable Markov decision processes. The approach begins with a base solution that assumes full observability. The partially observable solution is incrementally constructed by considering increasing amounts of information from observations. The base solution directs the expansion of the plan by providing an evaluation function ...

Interactive partially observable Markov decision processes (I-POMDP) provide a formal framework for planning for a self-interested agent in multiagent settings. An agent operating in a multiagent environment must deliberate about the actions that other agents may take and the effect these actions have on the environment and the rewards it receives. Traditional I-POMDPs model this dependence on ...