By Gerald Tesauro, David S. Touretzky, Todd K. Leen
November 28-December 1, 1994, Denver, Colorado NIPS is the longest working annual assembly dedicated to Neural details Processing platforms. Drawing on such disparate domain names as neuroscience, cognitive technological know-how, desktop technological know-how, records, arithmetic, engineering, and theoretical physics, the papers gathered within the lawsuits of NIPS7 mirror the iconic clinical and useful benefit of a broad-based, inclusive method of neural details processing. the first concentration continues to be the learn of a large choice of studying algorithms and architectures, for either supervised and unsupervised studying. The 139 contributions are divided into 8 components: Cognitive technology, Neuroscience, studying conception, Algorithms and Architectures, Implementations, Speech and sign Processing, visible Processing, and functions. subject matters of specific curiosity comprise the research of recurrent nets, connections to HMMs and the EM method, and reinforcement- studying algorithms and the relation to dynamic programming. at the theoretical entrance, growth is pronounced within the concept of generalization, regularization, combining a number of versions, and energetic studying. Neuroscientific experiences variety from the large-scale structures equivalent to visible cortex to single-cell electrotonic constitution, and paintings in cognitive medical is heavily tied to underlying neural constraints. There also are many novel functions corresponding to tokamak plasma keep watch over, Glove-Talk, and hand monitoring, and various implementations, with specific specialize in analog VLSI.
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S, a) is defined), then a is applicable in s, with γ (s, a) being the predicted outcome. Otherwise a is inapplicable in s. r cost : S × A → [0, ∞) is a partial function having the same domain as γ . Although we call it the cost function, its meaning is arbitrary: it may represent monetary cost, time, or something else that one might want to minimize. , if = (S, A, γ )), then cost(s, a) = 1 whenever γ (s, a) is defined. 1 requires a set of restrictive assumptions called the classical planning assumptions: 1.
An ; π. π = π . 17. A plan π = a1 , a2 , . . , an is applicable in a state s0 if there are states s1 , . . , sn such that γ (si−1 , ai ) = si for i = 1, . . , n. In this case, we define γ (s0 , π ) = sn ; γ (s0 , π ) = s0 , . . , sn . As a special case, the empty plan γ (s, ) = s . is applicable in every state s, with γ (s, ) = s and In the preceding, γ is called the closure of γ . In addition to the predicted final state, it includes all of the predicted intermediate states. 18. A state-variable planning problem is a triple P = ( , s0 , g), where is a state-variable planning domain, s0 is a state called the initial state, and g is a set of ground literals called the goal.
10. Let R and X be sets of rigid relations and state variables over a set of objects B, and S be a state-variable state space over X . An action template5 for S is a tuple α = (head(α), pre(α), eff(α), cost(α)) or α = (head(α), pre(α), eff(α)), the elements of which are as follows: r head(α) is a syntactic expression6 of the form act(z1 , z2 , . . , zk ), where act is a symbol called the action name, and z1 , z2 , . . , zk are variables called parameters. The parameters must include all of the variables (here we mean ordinary variables, not state variables) that appear anywhere in pre(α) and eff(α).