Towards multi-swarm problem solving in networks

White, Tony; Pagurek, B.

doi:10.1109/icmas.1998.699217

Cited by 84 publications

(41 citation statements)

References 11 publications

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“…Swarm intelligence, in terms of artificial intelligence, represents the ability of systems composed of non-intelligent agents with limited individual abilities, to achieve general intelligent behaviour of the conjuncted swarm [26]. The agent is at this time known as an individual that is able to sense and observe its surroundings and consequently undertake some of the available actions.…”

Section: Self-organization In Biological Systemsmentioning

confidence: 99%

Bee Hive at Work: Following a Developing Story on the Web

Návrat

Ezzeddine

2010

Artificial Intelligence in Theory and Practice III

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Problem solving methods, inspired biologically or by nature, are gaining in importance. Their potential for devising new, but also for increasing efficiency of some well-known methods for solving different kinds of problems, is becoming more and more obvious. The behaviour of social insects provides a lot of inspiration. We took inspiration from honey bees, in particular the way they behave in a hive. We proposed a modified model devised for searching parts of the web online. In a series of experiments, we attempted to follow a developing story with the aim to automatically find documents (web pages) bringing news on the story. This service, working as a beehive, delivered documents as expected.

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Section: Self-organization In Biological Systemsmentioning

confidence: 99%

Bee Hive at Work: Following a Developing Story on the Web

Návrat

Ezzeddine

2010

Artificial Intelligence in Theory and Practice III

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“…A global intelligence comes out as these agents communicate locally among themselves. Their interaction with their immediate environment produces a global intelligence as end [3]. Lightweight agents can be described as agents that easily accomplish their goal with a very negligible code size.…”

Section: Lightweight Agentsmentioning

confidence: 99%

Lightweight Agents, Intelligent Mobile Agent and RPC Schemes: A Comparative Analysis

Olajubu

Aderounmu

Ajayi

2011

CIT

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This paper presents the performance comparison of Lightweight Agents, Single Mobile Intelligent Agents and Remote Procedure Call which are tools for implementing communication in a distributed computing environment. Routing algorithms for each scheme is modeled based on TSP. The performance comparison among the three schemes is based on bandwidth overhead with retransmission, system throughput and system latency. The mathematical model for each performance metric is presented, from which mathematical model is derived for each scheme for comparison. The simulation results show that the LWAs has better performance than the other two schemes in terms of small bandwidth retransmission overhead, high system throughput and low system latency. The Bernoulli random variable is used to model the failure rate of the simulated network which is assumed to have probability of success p = 85% and the probability of failure q = 15%. The network availability is realized by multiplicative pseudorandom number generator during the simulation. The results of simulation are presented.

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“…''Swarm intelligence is a property of systems of unintelligent agents of limited individual capabilities exhibiting collectively intelligent behavior'' (White & Pagurek, 1998). Artificial swarm intelligence includes designing algorithms or distributed problemsolving devices inspired by the collective behavior of social insects and other animal societies (Bonabeau, Dorigo, & Théraulaz, 2000).…”

Section: Swarm Of Trackersmentioning

confidence: 99%

Oscillatory synchronization model of attention to moving objects

Yılmaz¹

2012

Neural Networks

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a b s t r a c tThe world is a dynamic environment hence it is important for the visual system to be able to deploy attention on moving objects and attentively track them. Psychophysical experiments indicate that processes of both attentional enhancement and inhibition are spatially focused on the moving objects; however the mechanisms of these processes are unknown. The studies indicate that the attentional selection of target objects is sustained via a feedforward-feedback loop in the visual cortical hierarchy and only the target objects are represented in attention-related areas. We suggest that feedback from the attention-related areas to early visual areas modulates the activity of neurons; establishes synchronization with respect to a common oscillatory signal for target items via excitatory feedback, and also establishes de-synchronization for distractor items via inhibitory feedback. A two layer computational neural network model with integrate-and-fire neurons is proposed and simulated for simple attentive tracking tasks. Consistent with previous modeling studies, we show that via temporal tagging of neural activity, distractors can be attentively suppressed from propagating to higher levels. However, simulations also suggest attentional enhancement of activity for distractors in the first layer which represents neural substrate dedicated for low level feature processing. Inspired by this enhancement mechanism, we developed a feature based object tracking algorithm with surround processing. Surround processing improved tracking performance by 57% in PETS 2001 dataset, via eliminating target features that are likely to suffer from faulty correspondence assignments.

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Towards multi-swarm problem solving in networks

Cited by 84 publications

References 11 publications

Bee Hive at Work: Following a Developing Story on the Web

Bee Hive at Work: Following a Developing Story on the Web

Lightweight Agents, Intelligent Mobile Agent and RPC Schemes: A Comparative Analysis

Oscillatory synchronization model of attention to moving objects

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