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20 Feb 2018: In Section 3, the grid-based ap-. proach to policy optimisation is introduced followedby a presentation of the k-nn Monte-Carlo policy op-timization in Section 4, along with an ... 5 ConclusionIn this paper, an extension to a grid-based policy

13 Mar 2018: PQ. RRSST>TU>U VWX>XY>YZ>Z[] >_ >abc. d>defg hi jk. l>lm. n>no>op>pq. rs ttuu.

15 Jun 2018: In standard systems this issue would be addressed by op-timising the language model scale factor.

20 Feb 2018: 5]. Here, we address the problem ofdecision-making. Moving from a limited domain dialogue system that op-erates on a relatively modest ontology to an open domain. ... 5. EXPERIMENTAL SET-UP. In order to examine the ability of the proposed method to

13 Mar 2018: Performance. By observing feedback from the robot, the op- erator is able to position the gripper to within lcm: sufficient accuracy to instruct it to pick up n small wootlcn Ilock

20 Feb 2018: 4.4 Conversational patterns. Given that the subjects felt the change of cognitiveload when they were talking to the system and op-erating the car simulator at the same time, we

20 Feb 2018: The op-tions for each random decision point are reason-able in the context in which it is encountered, buta uniform distribution of outcomes might not re-flect real user behaviour. ... Many of the decisions involvedare deterministic, allowing only one

20 Feb 2018: The ex-perimental results show that the policy op-timised in a restaurant search domain us-ing our domain-independent representa-tions can be deployed to a laptop sale do-main,

20 Feb 2018: the Q-function estimatewe expect during the process of learning and H is a linear op-erator that captures the reward lookahead from the Q-function(see Eq.

20 Feb 2018: increases. Pop op-erations are then performed where possible, the tree is prunedand identical nodes are joined so that the number stays constantor decreases. ... Error bars indicate 99% con-fidence intervals. This demonstrates the competitiveness of the