Self-organized Middle-Out Abstraction

Mammen, Sebastian von; Steghöfer, Jan-Philipp; Denzinger, Jörg; Jacob, Christian

doi:10.1007/978-3-642-19167-1_3

Cited by 6 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Early 2011 we presented the self-organised middle-out (SOMO) concept [von Mammen et al (2011)], an approach that automatically builds abstractions bottom-up and validates and revokes them top-down-possibly both at the same time but in respect to different model aspects. As it works in both directions and as it bridges the gap between the orders of the model, it can be considered to operate at the 'meso' level of analysis.…”

Section: Self-organising Middle-out Abstractionmentioning

confidence: 99%

Bring It On, Complexity! Present and Future of Self-Organising Middle-Out Abstraction

Mammen

Steghöfer

2014

The Computer After Me

Self Cite

View full text Add to dashboard Cite

Section: Self-organising Middle-out Abstractionmentioning

confidence: 99%

Bring It On, Complexity! Present and Future of Self-Organising Middle-Out Abstraction

Mammen

Steghöfer

2014

The Computer After Me

Self Cite

View full text Add to dashboard Cite

“…von Mammen et al introduced the concept of self-organized middle-out abstraction (SOMO) , where observer agents monitor the interaction history of sets of agents, use motif discovery to detect recurrent patterns, and create hierarchies of high-level agents that subsume the lower interacting agencies. 30 Although they do not exclude the possibility of a relationship between learned high-order patterns and emergent phenomena found in nature, SOMO primarily targets an increase of efficiency by repeatedly substituting groups of agents by individual high-level instances that work at lower computational costs.…”

Section: Related Workmentioning

confidence: 99%

Abstraction of agent interaction processes: Towards large-scale multi-agent models

2013

Self Cite

View full text Add to dashboard Cite

The typically large numbers of interactions in agent-based simulations come at considerable computational costs. In this article, we present an approach to reduce the number of interactions based on behavioural patterns that recur during runtime. We employ machine learning techniques to abstract the behaviour of groups of agents to cut down computational complexity while preserving the inherent flexibility of agent-based models. The learned abstractions, which subsume the underlying model agents’ interactions, are constantly tested for their validity: after all, the dynamics of a system may change over time to such an extent that previously learned patterns would not reoccur. An invalid abstraction is, therefore, removed again from the system. The creation and removal of abstractions continues throughout the course of a simulation in order to ensure an adequate adaptation to the system dynamics. Experimental results on biological agent-based simulations show that our proposed approach can successfully reduce the computational complexity during the simulation while maintaining the freedom of arbitrary interactions.

show abstract

“…As we imagined it to be primarily deployed in swarm systems, it was obvious to us that the abstractions should be discovered and managed by special swarm individuals that are immersed into arbitrary swarm simulations. We termed this concept self-organized middleout abstraction approach, or SOMO [50]-"middle-out" referring to the idea that it would create higher level representations (bottom-up) but also break them down again (top-down).…”

Section: Towards Self-organizing Hierarchiesmentioning

confidence: 99%

Optimization of Swarm-Based Simulations

Mammen

Shirazi

Sarpe

et al. 2012

ISRN Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

is properly cited.In computational swarms, large numbers of reactive agents are simulated. The swarm individuals may coordinate their movements in a "search space" to create efficient routes, to occupy niches, or to find the highest peaks. From a more general perspective though, swarms are a means of representation and computation to bridge the gap between local, individual interactions, and global, emergent phenomena. Computational swarms bear great advantages over other numeric methods, for instance, regarding their extensibility, potential for real-time interaction, dynamic interaction topologies, close translation between natural science theory and the computational model, and the integration of multiscale and multiphysics aspects. However, the more comprehensive a swarm-based model becomes, the more demanding its configuration and the more costly its computation become. In this paper, we present an approach to effectively configure and efficiently compute swarm-based simulations by means of heuristic, populationbased optimization techniques. We emphasize the commonalities of several of our recent studies that shed light on top-down model optimization and bottom-up abstraction techniques, culminating in a postulation of a general concept of self-organized optimization in swarm-based simulations.

show abstract

Self-organized Middle-Out Abstraction

Cited by 6 publications

References 11 publications

Bring It On, Complexity! Present and Future of Self-Organising Middle-Out Abstraction

Bring It On, Complexity! Present and Future of Self-Organising Middle-Out Abstraction

Abstraction of agent interaction processes: Towards large-scale multi-agent models

Optimization of Swarm-Based Simulations

Contact Info

Product

Resources

About