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20 Feb 2018: ruleset = ruledef";" { ruledef ";" } {dbasefile}ruledef = lassdef | lexdef lassdef = lassinst "->" [sub lass [ lassbody [ ond [prob lassbody = "(" [opt member { "," [opt member } ")"lexdef = lassinst "=" "(" atom[prob {"|" atom[prob ")"prob = "{"

20 Feb 2018: However, to generate an appropriate response it is onlynecessary for the system to decide between a few high level op-tions such as to confirm the last user input, ask for

20 Feb 2018: The advantage of this approach is that the op-timal transformation parameters can be determinedfrom the auxiliary function in a single pass over thedata[63].

20 Feb 2018: However,approx-imate solutions can still provide useful policies. The simplest ap-proach is to discretise belief space and then use standard MDP op-timisation methods [6].

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: NFp;. 2where Q is the set of all phone models and NF(p)the number of frames in the acoustic segment Op. ... Hence, the denominator score is de-termined by simply summing the log likelihood perframe over the duration of Op.

20 Feb 2018: S. sn. nsbsb. 1. )()(maxargˆ)( υππ (16). Thus the value-function method provides both a parti-tioning of belief space into regions corresponding to op-timal actions as well as the

20 Feb 2018: On-line active reward learning for policy op-timisation in spoken dialogue systems.

20 Feb 2018: Recent workby Graves et al. (2014) has demonstrated that anNN structure augmented with a carefully designedmemory block and differentiable read/write op-erations can learn to mimic computer programs.Moreover, the

20 Feb 2018: By op-timising directly against the desired objective func-tion such as BLEU score (Auli and Gao, 2014) orWord Error Rate (Kuo et al., 2002), the model canexplore its output space