Multiset of Agents in a Network for Simulation of Complex Systems

Murthy, V. K.; Krishnamurthy, E. V.

doi:10.1007/978-3-642-04227-0_6

Cited by 4 publications

(22 citation statements)

References 68 publications

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“…The multiset-agent system consists of several single agent-systems . Murthy and Krishnamurthy [19]. Thus if N agents are involved: i = 1,2, , , N, each of the agents will be denoted with a label (i).…”

Section: Multi-set Of Agents Paradigmmentioning

confidence: 99%

“…This behavior arises due to interaction among the agents and the environment, endless communicat ion and computation among agents and possibility for auto regulation. Such a system contains many cycles and the metric entropy (Lyapunov exponent) is positive-See Murthy, Krishnamurthy [19]. Such a system performs interactive-nonuniform co mputation with mu ltip le inputs and outputs, and varying memo ry and instructions.…”

Section: A Relation To Turing Machine Taking Advicementioning

confidence: 99%

“…The mu ltiset of agents paradigm (MAP) consists of the following features Murthy and Krishnamurthy [19]. (i) A mu ltiset M that contains evolving agents (called the agent-space) whose information is structured in an appropriate way to suit the problem at hand.…”

Section: B Features Of Agents Transactional Paradigmmentioning

confidence: 99%

“…Each rule consists of a left -hand side (a pattern or property or attribute) describing the conditions under which the agents can commun icate and interact, and a right hand side describes the actions to be performed by the agents, if the ru le becomes applicab le, based on deterministic, fuzzy or probabilistic criteria, Murthy and Krishnamurthy [19]. (iii) A control strategy that specifies the manner in which the agents will be chosen and interaction rules will be applied, the kinetics of the ru le-interference (inhibition, activation, diffusion, chemotaxis) and a way of resolving conflicts that may arise when several rules match at once.…”

Section: B Features Of Agents Transactional Paradigmmentioning

confidence: 99%

“…Hence, in p ractice, we need to use the concurrent programming (or mu ltithreading) style of t ransaction processing to handle these aspects effectively, with a built in determin istic, non-deterministic or stochastic control. The transaction processing approach among interacting agents permits concurrent or partial o rder or total order execution with suitable time synchronization, as well as, in animation, [19].…”

Section: E Agent-based Graph Motifsmentioning

confidence: 99%

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Agent-based Models in Synthetic Biology: Tools for Simulation and Prospects

Krishnamurthy¹

2012

IJISA

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Abstract-We describe a mult iset of agents based modeling and simulation paradig m for synthetic biology. The mu ltiset of agents -based programming paradig m, can be interpreted as the outcome arising out of deterministic, nondetermin istic or stochastic interaction among elements in a mult iset object space, that includes the environment. These interactions are like chemical reactions and the evolution of the mu ltiset can emulate the system biological functions. Since the reaction rules are inherently parallel, any number of actions can be performed cooperatively or competit ively among the subsets of elements, so that the elements evolve toward equilibriu m or emergent state. Pract ical realization of this paradigm for system biological simulat ion is achieved through the concept of transactional style programming with agents, as well as soft computing (neural-network) principles. Also we briefly describe currently available tools for agent-based-modeling, simu lation and animat ion.

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Section: Multi-set Of Agents Paradigmmentioning

confidence: 99%

Section: A Relation To Turing Machine Taking Advicementioning

confidence: 99%

Section: B Features Of Agents Transactional Paradigmmentioning

confidence: 99%

Section: B Features Of Agents Transactional Paradigmmentioning

confidence: 99%

Section: E Agent-based Graph Motifsmentioning

confidence: 99%

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Agent-based Models in Synthetic Biology: Tools for Simulation and Prospects

Krishnamurthy¹

2012

IJISA

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A Systems Approach to the Policy‐Level Risk Assessment of Exotic Animal Diseases: Network Model and Application to Classical Swine Fever

et al. 2012

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Exotic animal diseases (EADs) are characterized by their capacity to spread global distances, causing impacts on animal health and welfare with significant economic consequences. We offer a critique of current import risk analysis approaches employed in the EAD field, focusing on their capacity to assess complex systems at a policy level. To address the shortcomings identified, we propose a novel method providing a systematic analysis of the likelihood of a disease incursion, developed by reference to the multibarrier system employed for the United Kingdom. We apply the network model to a policy-level risk assessment of classical swine fever (CSF), a notifiable animal disease caused by the CSF virus. In doing so, we document and discuss a sequence of analyses that describe system vulnerabilities and reveal the critical control points (CCPs) for intervention, reducing the likelihood of U.K. pig herds being exposed to the CSF virus.

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