Paul A. Crook

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— For mobile robots, as well as other learning systems, the ability to highlight unexpected features of their environment – novelty detection – is very useful. One particularly important application for a robot equipped with novelty detection is inspection, highlighting potential problems in an environment. In this paper two novelty filters, both of which(More)
Due to the unavoidable fact that a robot's sensors will be limited in some manner, it is entirely possible that it can find itself unable to distinguish between differing states of the world. This confounding of states, also referred to as perceptual aliasing, has serious effects on the ability of reinforcement learning algorithms to learn stable policies.(More)
We point out several problems in scaling-up statistical approaches to spoken dialogue systems to enable them to deal with complex but natural user goals, such as disjunctive and negated goals and preferences. In particular, we explore restrictions imposed by current independence assumptions in POMDP dialogue models. This position paper proposes the use of(More)
We present a novel application of hypothesis ranking (HR) for the task of domain detection in a multi-domain, multi-turn dialog system. Alternate, domain dependent, semantic frames from a spoken language understanding (SLU) analysis are ranked using a gradient boosted decision trees (GBDT) ranker to determine the most likely domain. The ranker, trained(More)
We propose Inference Knowledge Graph, a novel approach of remapping existing, large scale, semantic knowledge graphs into Markov Random Fields in order to create user goal tracking models that could form part of a spoken dialog system. Since semantic knowledge graphs include both entities and their attributes, the proposed method merges the semantic(More)
This paper presents initial results in the application of Value Directed Compression (VDC) to spoken dialogue management belief states for reasoning about complex user goals. On a small but realistic SDS problem VDC generates a lossless compression which achieves a 6-fold reduction in the number of dialogue states required by a Partially Observable Markov(More)
This paper presents the first demonstration of a statistical spoken dialogue system that uses automatic belief compression to reason over complex user goal sets. Reasoning over the power set of possible user goals allows complex sets of user goals to be represented , which leads to more natural dialogues. The use of the power set results in a massive(More)
This paper sets out the concept of consistent exploration of observation-action pairs. We present a new temporal difference algorithm, CEQ(λ), based on this concept and demonstrate using a randomly generated set of partially observable Markov decision processes (POMDPs) that it outperforms SARSA(λ). This result should generalise to any POMDP where(More)
Due to the unavoidable fact that a robot's sensors will be limited in some manner, it is entirely possible that it can find itself unable to distinguish between differing states of the world (the world is in effect partially observable). If reinforcement learning is used to train the robot, then this confounding of states can have a serious effect on its(More)