By Luc Steels, Guus Schreiber, Walter Van de Velde

ISBN-10: 3540584870

ISBN-13: 9783540584872

This quantity includes a variety of the foremost papers provided on the 8th ecu wisdom Acquisition Workshop (EKAW '94), held in Hoegaarden, Belgium in September 1994.

The e-book demonstrates that paintings within the mainstream of information acquisition ends up in helpful useful effects and places the data acquisition company in a broader theoretical and technological context. The 21 revised complete papers are rigorously chosen key contributions; they tackle wisdom modelling frameworks, the id of wide-spread parts, method points, and architectures and purposes. the amount opens with a considerable preface by means of the amount editors surveying the contents.

**Read or Download A Future for Knowledge Acquisition: 8th European Knowledge Acquisition Workshop, EKAW '94 Hoegaarden, Belgium, September 26–29, 1994 Proceedings PDF**

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**Extra resources for A Future for Knowledge Acquisition: 8th European Knowledge Acquisition Workshop, EKAW '94 Hoegaarden, Belgium, September 26–29, 1994 Proceedings**

**Sample text**

Y)V(y) K L yEX P(x, y)cP~()* LINEAR LEAST-SQUARES ALGORITHMS FOR TEMPORAL DIFFERENCE LEARNING t L 41 (9) P(r, Y)¢y)'8*, yEX for every state x EX. 1. The scalar output, Tx, is the inner product of an input vector, ¢x 'Y LYE X P(x, y)cfJ y, and the true pmametel vector, ()* .. For each time step t, we therefore have the following equation that has the same form as (S): (10) where rt is the reward received on the transition from Xt to Xt+l. (rt - Tt) corresponds to the noise term T/t in (S). t - For any Markov chain, if x and yare states such that P(x, y) > 0, with T/xy = R(x,y) - Tx and Wx = (¢x - ,2: yE x P(x,y)¢y), then E{T/} = 0, and Cor(w, 1/) - O.

O"TD is a measure of the noise that is inherent in any TD learning algorithm, even after the parameters have converged to 8*. ce rate of a TD algorithm depends linearly on O"TD(Figure 4). This relationship is very clear for RLS TD, but also seems to hold for NTD(A) for larger (JTD' The theorems concerning convergence of LS TD (and RLS TD) can be generalized in at least two ways. First, the immediate rewards can be random variables instead of constants. R(x. y) would then designate the expected reward of a transition from state x to state y The second cbange involves tbe way the states (and state transitions) are sampled.

1. The scalar output, Tx, is the inner product of an input vector, ¢x 'Y LYE X P(x, y)cfJ y, and the true pmametel vector, ()* .. For each time step t, we therefore have the following equation that has the same form as (S): (10) where rt is the reward received on the transition from Xt to Xt+l. (rt - Tt) corresponds to the noise term T/t in (S). t - For any Markov chain, if x and yare states such that P(x, y) > 0, with T/xy = R(x,y) - Tx and Wx = (¢x - ,2: yE x P(x,y)¢y), then E{T/} = 0, and Cor(w, 1/) - O.

### A Future for Knowledge Acquisition: 8th European Knowledge Acquisition Workshop, EKAW '94 Hoegaarden, Belgium, September 26–29, 1994 Proceedings by Luc Steels, Guus Schreiber, Walter Van de Velde

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