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13 Mar 2018: áà-ØÙcä)Ü@ÝØàØÙ6ØäÙcÚä_ÙcÚàåfä_ØÞÛÝÜ@ÙcØáâSàÕ'Ü!Kä/WÕcß)Þ ØÙÜ@áà+)ÚÕ_Ø-ØÈáâÙcMÜ@ÙØÙàßÚäØáàÚÚàQâ)ÚáÚ ... áÙcÚ2Öß)ØÖÚß:äcÖ

20 Feb 2018: The kernelfunction between two sets of actions is. kA(aB,aE) = δaB(a. ... 24, no. 4, pp. 562–588, 2010. [6] M. Gašić, C. Breslin, M.

20 Feb 2018: kA(a,a′) = δa(a. ′) (7). where δa(a′) = 1 iff a = a′, 0 otherwise. ... 24, no. 4,pp. 562–588, 2010. [18] T Jebara, R Kondor, and A Howard, “Probability prod-uct kernels,” J.

20 Feb 2018: 1.2% 2.0%Request 17.4% 24.5% 18.4% 24.4%. ... 24, no. 4, pp. 562–588, 2010. [22] J Peters and S Schaal, “Natural Actor-Critic,” Neurocomput-ing, vol.

20 Feb 2018: kA(a,a′) = δa(a. ′) (5). where δa(a′) = 1 iff a = a′, 0 otherwise. ... 24, no. 4, pp. 562–588, 2010. [13] M Gašić, C Breslin, M Henderson, D Kim, M Szummer,B Thomson, P Tsiakoulis, and S Young, “POMDP-based dia-logue manager adaptation

20 Feb 2018: kA(a,a′). For a sequence of belief state-. action pairs Bt = [(b0,a0),. ... For the action space kernel, the δ-kernel is useddefined by:. kA(a,a′) = δa(a. ′).

20 Feb 2018: kA(a,a′) = δa(a. ′) (5). where δa(a′) = 1 iff a = a′, 0 otherwise. ... 24, no. 4, pp. 562–588, 2010. [13] M Gašić, C Breslin, M Henderson, D Kim, M Szummer,B Thomson, P Tsiakoulis, and S Young, “POMDP-based dia-logue manager adaptation

20 Feb 2018: is factored into separate kernels over thesummary state and action spaces kC(c,c′)kA(a,a′). ... 3.3. The agenda-based simulated user. The agenda-based user simulator [24, 25] factorises the user stateinto an agenda and a goal.

20 Feb 2018: 211. 2121. 1. aannna. annnnn. ka. rd (13). Here nr specifies the number of events that occurred r times and a fixed discounting factor was used if they are zero. ... 52 class n-gram 26.3 25.0 24.9 HVS_52 21.7 20.4 20.1. Table 1: Perplexity for models of

20 Feb 2018: kA(a,a′). For a training sequence of belief state-action pairs B = [(b0,a0),. , ... kA(a,a′) = δa(a. ′) (6). where δa(a′) = 1 iff a = a′, 0 otherwise.

20 Feb 2018: function,Q(b, a) GP (0, k((b, a), (b, a))) wherethe kernelk(, ) is factored into separate kernels over thesummary state and action spaceskB(b, b)kA(a, a). ... 24, no. 4, pp. 562–588, 2010. [10] M. Gǎsić, S. Keizer, F.

20 Feb 2018: The summary action kernel is defined as:. kA(a,a′) = δa(a. ′) (3). ... 24. Figure 13: The number of times each system queries the user for feedback during on-linepolicy optimisation as a function of the number of training dialogues.

20 Feb 2018: Thomson and Young2010] Blaise Thomson and SteveYoung. 2010. Bayesian update of dialogue state:A pomdp framework for spoken dialogue systems.Computer Speech and Language, 24:562–588. ... Zhang and Chaudhuri2015] Chicheng Zhang and Ka-malika Chaudhuri.

13 Mar 2018: áà-ØÙcä)Ü@ÝØàØÙ6ØäÙcÚä_ÙcÚàåfä_ØÞÛÝÜ@ÙcØáâSàÕ'Ü!Kä/WÕcß)Þ ØÙÜ@áà+)ÚÕ_Ø-ØÈáâÙcMÜ@ÙØÙàßÚäØáàÚÚàQâ)ÚáÚ ... áÙcÚ2Öß)ØÖÚß:äcÖ

20 Feb 2018: To obtain a closed formsolution of (24), the policy π must be differentiable with respect to θ. ... 10. To lower the variance of the estimate of the gradient, a constant baseline, B, can beintroduced into (24) without introducing any bias [22].

20 Feb 2018: An advantage of this sparsification approach is that itenables non-positive definite kernel functions to be used in theapproximation, for example see [24]. ... It has already beenshown that active learning has the potential to lead to fasterlearning [24]

13 Mar 2018: t=20. t=22. t=24. t=26t=28Probabilistic. Space Carving. Error (RGB distance). Per. cent.

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13 Mar 2018: t=20. t=22. t=24. t=26t=28Probabilistic. Space Carving. Error (RGB distance). Per. cent.

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