While symbolic learning approaches encode the knowledge provided by the presentation of the cases explicitly into a symbolic representation of the concept, e.g. formulas, rules, or decision trees, case-based approaches describe learned concepts implicitly by a pair (CB; d), i.e. by a set C B of cases and a distance measure d. Given the same information, symbolic as well as the case-based approa...

We study an individual who faces a dynamic decision problem in which the process of information arrival is unobserved by the analyst. We derive two utility representations of preferences over menus of acts that capture the individual’s uncertainty about his future beliefs. The most general representation identi…es a unique probability distribution over the set of posteriors that the decision ma...

Regret-based dynamics have been introduced and studied in the context of discrete-time repeated play. Here we carry out the corresponding analysis in continuous time. We observe that, in contrast to (smooth) fictitious play or to evolutionary models, the appropriate state space for this analysis is the space of distributions on the product of the players’ pure action spaces (rather than the pro...

We formulate a general theory of decision making based on a lattice of observable events, and we exhibit a large class of representations called the general model. Some of the representations are equivalent to the so called standard model in which observable events are modelled by an algebra of measurable subsets of a state space, while others are not compatible with such a description. We show...

The field of reinforcement learning (RL) has achieved great strides in learning control knowledge from closed-loop interaction with environments. “Classical” RL, based on atomic state space representations, suffers from an inability to adapt to nonstationarities in the target Markov decision process (i.e., environment). Relational RL is widely seen as being a potential solution to this shortcom...

Binary Decision Diagram (in short BDD) is an efficient data structure which has been used widely in computer science and engineering. BDD-based attack in key stream cryptanalysis is one of the best forms of attack in its category. In this paper, we propose a new key stream attack which is based on ZDD(Zero-suppressed BDD). We show how a ZDD-based key stream attack is more efficient in time and ...

Background: Epilepsy is a brain disorder that changes the basin geometry of the oscillation of trajectories in the phase space. Nevertheless, recent studies on epilepsy often used the statistical characteristics of this space to diagnose epileptic seizures. Objectives: We evaluated changes caused by the seizures on the mentioned basin by focusing on phase space sorted by Poincaré sections. Ma...

exists (is computable) for each x in H; if P is that projection, / is the identity operator on H, and the adjoint T*ofT exists, then / P is the projection of H on ker(3*), the kernel of T*. (For an example to show that the existence of the adjoint of a bounded operator on a Hilbert space is not automatic in constructive mathematics, see Brouwerian Example 3 in [7]; see also Example 2 below.) Th...

Extending a result of Borodin, et. al. [l], we show that any branching program using linear queries " rhixi: c " to sort n numibers i ⌧pp l **,⌧n must satisfy the time-space tradeoff relation TS = n(n*) , The same relation is also shown to be true for branching programs that uses queries " min R = ? " where R is any subset of {xl,x2,...,xnj .