Israel A. Wagner

Bio: Israel A. Wagner is an academic researcher from IBM. The author has contributed to research in topics: Logic gate & Very-large-scale integration. The author has an hindex of 26, co-authored 68 publications receiving 2333 citations. Previous affiliations of Israel A. Wagner include Technion – Israel Institute of Technology & University of Haifa.

Distributed covering by ant-robots using evaporating traces

Abstract: We investigate the ability of a group of robots, that communicate by leaving traces, to perform the task of cleaning the floor of an un-mapped building, or any task that requires the traversal of an unknown region. More specifically, we consider robots which leave chemical odour traces that evaporate with time, and are able to evaluate the strength of smell at every point they reach, with some measurement error. Our abstract model is a decentralized multi-agent adaptive system with a shared memory, moving on a graph whose vertices are the floor-tiles. We describe three methods of covering a graph in a distributed fashion, using smell traces that gradually vanish with time, and show that they all result in eventual task completion, two of them in a time polynomial in the number of tiles. Our algorithms can complete the traversal of the graph even if some of the agents die or the graph changes during the execution, as long as the graph stays connected. Another advantage of our agent interaction processes is the ability of agents to use noisy information at the cost of longer cover time.

Gate-diffusion input (GDI): a power-efficient method for digital combinatorial circuits

Abstract: Gate diffusion input (GDI) - a new technique of low-power digital combinatorial circuit design - is described. This technique allows reducing power consumption, propagation delay, and area of digital circuits while maintaining low complexity of logic design. Performance comparison with traditional CMOS and various pass-transistor logic design techniques is presented. The different methods are compared with respect to the layout area, number of devices, delay, and power dissipation. Issues like technology compatibility, top-down design, and precomputing synthesis are discussed, showing advantages and drawbacks of GDI compared to other methods. Several logic circuits have been implemented in various design styles. Their properties are discussed, simulation results are reported, and measurements of a test chip are presented.

Cooperative Cleaners: A Study in Ant Robotics

Abstract: In the world of living creatures, simple-minded animals often cooperate to achieve common goals with amazing performance. One can consider this idea in the context of robotics, and suggest models for programming goal-oriented behavior into the members of a group of simple robots lacking global supervision. This can be done by controlling the local interactions between the robot agents, to have them jointly carry out a given mission. As a test case we analyze the problem of many simple robots cooperating to clean the dirty floor of a non-convex region in Z2, using the dirt on the floor as the main means of inter-robot communication.

A Distributed Ant Algorithm for\protect Efficiently Patrolling a Network

Abstract: We consider the problem of patrolling—i.e. ongoing exploration of a network by a decentralized group of simple memoryless robotic agents. The model for the network is an undirected graph, and our goal, beyond complete exploration, is to achieve close to uniform frequency of traversal of the graph’s edges. A simple multi-agent exploration algorithm is presented and analyzed. It is shown that a single agent following this procedure enters, after a transient period, a periodic motion which is an extended Eulerian cycle, during which all edges are traversed an identical number of times. We further prove that if the network is Eulerian, a single agent goes into an Eulerian cycle within 2|E|D steps, |E| being the number of edges in the graph and D being its diameter. For a team of k agents, we show that after at most 2( 1 + 1/k) |E|D steps the numbers of edge visits in the network are balanced up to a factor of two. In addition, various aspects of the algorithm are demonstrated by simulations.

Logic circuit and method of logic circuit design

Abstract: A complementary logic circuit contains a first logic input, a second logic input, a first dedicated logic terminal, a second dedicated logic terminal, a first logic block, and a second logic block. The first logic block consists of a network of p-type transistors for implementing a predetermined logic function. The p-type transistor network has an outer diffusion connection, a first network gate connection, and an inner diffusion connection. The outer diffusion connection of the p-type transistor network is connected to the first dedicated logic terminal, and the first network gate connection of the p-type transistor network is connected to the first logic input. The second logic block consists of a network of n-type transistors which implements a logic function complementary to the logic function implemented by the first logic block. The n-type transistor network has an outer diffusion connection, a first network gate connection, and an inner diffusion connection. The outer diffusion connection of the n-type transistor network is connected to the second dedicated logic terminal, and the first network gate connection of the n-type transistor network is connected to the second logic input. The inner diffusion connections of the p-type network and of the n-type network are connected together to form a common diffusion logic terminal.

Ant Colony Optimization

Abstract: Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization Ant colony optimization (ACO) takes inspiration from the foraging behavior of some ant species These ants deposit pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony Ant colony optimization exploits a similar mechanism for solving optimization problems From the early nineties, when the first ant colony optimization algorithm was proposed, ACO attracted the attention of increasing numbers of researchers and many successful applications are now available Moreover, a substantial corpus of theoretical results is becoming available that provides useful guidelines to researchers and practitioners in further applications of ACO The goal of this article is to introduce ant colony optimization and to survey its most notable applications

Ant algorithms for discrete optimization

Abstract: This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic.

Ant Colony Optimization

Abstract: Ant Colony Optimization (ACO) is a stochastic local search method that has been inspired by the pheromone trail laying and following behavior of some ant species [1]. Artificial ants in ACO essentially are randomized construction procedures that generate solutions based on (artificial) pheromone trails and heuristic information that are associated to solution components. Since the first ACO algorithm has been proposed in 1991, this algorithmic method has attracted a large number of researchers and in the meantime it has reached a significant level of maturity. In fact, ACO is now a well-established search technique for tackling a wide variety of computationally hard problems.

Ant colony optimization: artificial ants as a computational intelligence technique

Abstract: The introduction of ant colony optimization (ACO) and to survey its most notable applications are discussed. Ant colony optimization takes inspiration from the forging behavior of some ant species. These ants deposit Pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony. The model proposed by Deneubourg and co-workers for explaining the foraging behavior of ants is the main source of inspiration for the development of ant colony optimization. In ACO a number of artificial ants build solutions to an optimization problem and exchange information on their quality through a communication scheme that is reminiscent of the one adopted by real ants. ACO algorithms is introduced and all ACO algorithms share the same idea and the ACO is formalized into a meta-heuristics for combinatorial problems. It is foreseeable that future research on ACO will focus more strongly on rich optimization problems that include stochasticity.