We provide an overview of some recent progress on the complexity of election systems. The issues studied include the complexity of the winner, manipulation, bribery, and control problems.

The management and combination of uncertain, imprecise, fuzzy and even paradoxical or high conflicting sources of information has always been, and still remains today, of primal importance for the development of reliable modern information systems involving artificial reasoning. In this introduction, we present a survey of our recent theory of plausible and paradoxical reasoning, known as Dezert-Smarandache Theory (DSmT) in the literature, developed for dealing with imprecise, uncertain and paradoxical sources of information. We focus our presentation here rather on the foundations of DSmT, and on the two important new rules of combination, than on browsing specific applications of DSmT available in literature. Several simple examples are given throughout the presentation to show the efficiency and the generality of this new approach.

In this report, a novel approach to intelligence and learning is introduced; this approach is based upon what we called percep tion logic. W h at we call ' perception automata ' is introduced in which learning is accom p lished at different perception resolution. Learning in this autom a ta is not heuristic, rather it guarantees the convergence of the approxim a ted function to whatever precision required. Furthe rm ore, the learning process can take place on-line and in at m o st O(log(N)) epochs, where N is the num ber of sam p les. The perception autom a ta is based on hierarchal leve ls of resolution in which each level adds som e details to the constructed function till th e final level can successfully reconstruct the whole function. This approach com b ines the favors of com putational approach in the sense that it is precise, structural and rigorous, and the features of distributed processing and adaptivity of soft com puting, as well as continuity and real-tim e response of dynam i cal system s.

Instantaneously Trained Neural Networks Abhilash Ponnath Abstract: This paper presents a review of instantaneously trained neural networks (ITNNs). These networks trade learning time for size and, in the basic model, a new hidden node is created for each training sample. Various versions of the corner-classification family of ITNNs, which have f ound applications in artificial intelligence (AI), are described. Implementation issues are also considered. 1 Introduction The human brain, the most complex known living structure in the universe, has the nerve cell or neuron as its fundamental unit. The number of neurons and connections between the neurons is enormous; this ensemble enables the brain to surpass the computational capacity of supercomputers in existence today. Artificial neural networks (ANNs) are models of the brain, which implement the mapping, ƒ: X Y such that the task is completed in a "certain" sense.

Computational Creativity: Coming of Age

Such creative software can be used for autonomous creative tasks, such as inventing mathematical theories, writing poems, painting pictures, and composing music. However, computational creativity studies also enable us to understand human creativity and to produce programs for creative people to use, where the software acts as a creative collaborator rather than a mere tool. Historically, it's been difficult for society to come to terms with machines that purport to be intelligent and even more difficult to admit that they might be creative. For instance, in 1934, some professors at the University of Manchester in the United Kingdom built meccano models that were able to solve some mathematical equations. Groundbreaking for its time, this project was written up in a piece in Meccano Magazine. The article was titled "Are Thinking Machines Possible" and was very upbeat, but surprisingly ends by stating that "Truly creative thinking of course will always remain beyond the power of any machine." Surely, though, this attitude has changed in light of the amazing advances in hardware and software technology that followed those meccano models?

The recently introduced series of description logics under the common moniker `DL-Lite' has attracted attention of the description logic and semantic web communities due to the low computational complexity of inference, on the one hand, and the ability to represent conceptual modeling formalisms, on the other. The main aim of this article is to carry out a thorough and systematic investigation of inference in extensions of the original DL-Lite logics along five axes: by (i) adding the Boolean connectives and (ii) number restrictions to concept constructs, (iii) allowing role hierarchies, (iv) allowing role disjointness, symmetry, asymmetry, reflexivity, irreflexivity and transitivity constraints, and (v) adopting or dropping the unique same assumption. We analyze the combined complexity of satisfiability for the resulting logics, as well as the data complexity of instance checking and answering positive existential queries. Our approach is based on embedding DL-Lite logics in suitable fragments of the one-variable first-order logic, which provides useful insights into their properties and, in particular, computational behavior.

Computational techniques have shown much promise in the field of Finance, owing to their ability to extract sense out of dauntingly complex systems. This paper reviews the most promising of these techniques, from traditional computational intelligence methods to their machine learning siblings, with particular view to their application in optimising the management of a portfolio of financial instruments. The current state of the art is assessed, and prospective further work is assessed and recommended.

This paper reviews the functional aspects of statistical learning theory. The main point under consideration is the nature of the hypothesis set when no prior information is available but data. Within this framework we first discuss about the hypothesis set: it is a vectorial space, it is a set of pointwise defined functions, and the evaluation functional on this set is a continuous mapping. Based on these principles an original theory is developed generalizing the notion of reproduction kernel Hilbert space to non hilbertian sets. Then it is shown that the hypothesis set of any learning machine has to be a generalized reproducing set. Therefore, thanks to a general "representer theorem", the solution of the learning problem is still a linear combination of a kernel. Furthermore, a way to design these kernels is given. To illustrate this framework some examples of such reproducing sets and kernels are given.

We study novel approaches for solving of hard combinatorial problems by translation to Boolean Satisfiability (SAT). Our focus is on combinatorial problems that can be represented as a permutation of n objects, subject to additional constraints. In the case of the Hamiltonian Cycle Problem (HCP), these constraints are that two adjacent nodes in a permutation should also be neighbors in the original graph for which we search for a Hamiltonian cycle. We use the absolute SAT encoding of permutations, where for each of the n objects and each of its pos- sible positions in a permutation, a predicate is defined to indicate whether the object is placed in that position. For implementation of this predicate, we compare the direct and logarithmic encodings that have been used previously, against 16 hierarchical parameterizable encodings of which we explore 416 instantiations. We propose the use of enumerative adjacency constraints—that enumerate the possible neighbors of a node in a permutation — instead of, or in addition to the exclusivity adjacency constraints — that exclude impossible neighbors, and that have been applied previously. We study 11 heuristics for efficiently choosing the first node in the Hamiltonian cycle, as well as 8 heuristics for static CNF variable ordering. We achieve at least 4 orders of magnitude average speedup on HCP benchmarks from the phase transition region, relative to the previously used encodings for solving of HCPs via SAT, such that the speedup is increasing with the size of the graphs.