Eugene Eberbach

Turing’s Ideas and Models of Computation

The theory of computation that we have inherited from the 1960s focuses on algorithmic computation as embodied in the Turing Machine to the exclusion of other types of computation that Turing had considered. In this chapter we present new models of computation, inspired by Turing’s ideas, that are more appropriate for today’s interactive, networked, and embedded computing systems. These models represent super-Turing computation, going beyond Turing Machines and algorithms. We identify three principles underlying super-Turing computation (interaction with the world, infinity of resources, and evolution of systems) and apply these principles in our discussion of the implications of super-Turing computation for the future of computer science.

New Models of Computation

This paper examines the limitations of Turing Machines as a complete model of computation, and presents several models that extend Turing Machines. Dynamic interaction of clients and servers on the Internet, an infinite adaptation from evolutionary computation, and robots sensing and acting are some examples of areas that cannot be properly described using Turing Machines and algorithms. They require new models of computation going beyond Turing Machines. We refer to such new models as superTuring models of computation. Three superTuring models of computation, namely Interaction Machines, the π calculus and the

Evolutionary Automata

-calculus are presented and explained, focussing on why they are better for the solution of computational problems. We expect that superTuring computation will become the central programming paradigm in the future.

Anytime Algorithm for Feature Selection

Expressiveness and convergence of evolutionary computation (EC) is studied using the evolutionary automata model. It turns out that all standard classes of evolutionary automata are equally expressive when they operate in the terminal mode, i.e. in the terminal mode, evolutionary finite automata (EFA) are as expressive as evolutionary pushdown automata, evolutionary linearly bounded automata, evolutionary Turing machines or evolutionary inductive Turing machines. For example, the simplest class of evolutionary automata, EFA, can accept all recursively enumerable languages (i.e. EFA have power of Turing machines) and even more—they can accept languages that are not recursively enumerable. Due to utilization of evolutionary automata, we obtain also very simple sufficient conditions for convergence of EC.

Computing Models for Distributed Autonomic Clouds and Grids in the Context of the DIME Network Architecture

Feature selection is used to improve performance of learning algorithms by finding a minimal subset of relevant features. Since the process of feature selection is computationally intensive, a trade-off between the quality of the selected subset and the computation time is required. In this paper, we are presenting a novel, anytime algorithm for feature selection, which gradually improves the quality of results by increasing the computation time. The algorithm is interruptible, i.e., it can be stopped at any time and provide a partial subset of selected features. The quality of results is monitored by a new measure: fuzzy information gain. The algorithm performance is evaluated on several benchmark datasets.

Expressiveness of

This paper presents an overview of computing models for a very important class of distributed systems: autonomic grids and clouds. We present the DIME network architecture as a representative of this still relatively new class of computing. We attempt to capture its potentials by formal modeling and emerging properties.

The

-calculus is a higher-order polyadic process algebra for resource bounded computation. It has been designed to handle autonomous agents, evolutionary computing, neural nets, expert systems, machine learning, and distributed interactive AI systems, in general.

-calculus process algebra for problem solving: A paradigmatic shift in handling hard computational problems

-calculus has built-in cost-optimization mechanism allowing to deal with nondeterminism, incomplete and uncertain information. In this paper, we investigate expressiveness of

A generic tool for distributed AI with matching as message passing

-calculus. We show that due to infinitary means, it allows to express models having richer behavior than Turing machine, including cellular automata, interaction machines, neural networks, and random automata networks. We also investigate the importance of synchronization, representation of continuity, and higher-order.