SeSAm
Category Cross-Omics>Agent-Based Modeling/Simulation/Tools
Abstract SeSAm (Shell for Simulated Agent Systems) is a Multi-Agent Simulation (MAS) environment system that provides a generic environment for modeling and experimenting with agent-based simulation.
The main focus is to enable scientists to construct models by 'visual programming'.
The manufacturers specially focused on providing a tool for the easy construction of complex models which include dynamic interdependencies or emergent behavior.
SeSAm is a tool for 'agent based simulation'. Agents are active entities of the simulation and their behavior is implemented with activity diagrams.
Based on an extensive number of primitive components, a user is able to design a simulation graphically without knowing the syntax of a traditional programming language.
The model specification is executable in the same environment and the dynamics of this simulation may be observed.
As there are freely configurable instruments for gathering data and scripting options for constructing simulation experiments, SeSAm is a highly valuable tool for MAS simulations especially for complex models with flexible agent behavior and interactions.
SeSAm is useful for simulations in many application domains, such as:
1) Logistics (coordination, storage layout optimization, software testing, etc.);
2) Production (factory, optimized plans for different requirements, etc.);
3) Traffic (avoidance of traffic jams, traffic light control, route choice, etc.);
4) Passenger Flow (market improvement, evacuation of buildings);
5) Health Care (optimization of clinical processes, reduction of costs).
6) Biology (understanding of insect behavior, testing theories)
7) And many other domains.
SeSAm features/capabilities provide:
1) Easy visual agent modeling --
SeSAm is completely based on 'visual agent modeling'. That means that you don't have to know or learn a programming language, though some kind of structured thinking is useful.
The user can create Agents by modeling their behavior with activity diagrams and situations can be created by placing agents in a two (2) dimensional map.
The behavior of agents in SeSAm is described visually by activity diagrams. SeSAm activity diagrams are very similar to UML-Activity diagrams.
Activities contain actions that are executed when being in this activity. Activities have exit rules (or exit arrows), to indicate when the next activity has to be chosen.
2) Flexible environment and situation definition --
A "Situation" is constructed of several agents’ places on a 2- dimensional map. A situation is also the initial setting for a simulation.
3) The whole power of a programming language --
You can model almost anything in SeSAm because the underlying languages SeSAm-Impl and SeSAm-UML are Turing-complete.
Many concepts of traditional programming have been adopted and are provided in an environment for visual modeling, including:
- a) Abstract data types;
- b) Complex (recursive) functions;
- c) Many primitive data types; and
- d) A Function set that ensures equivalence to Partial Recursive Functions.
4) Integrated graphical simulation analysis --
SeSAm offers a lot of tools for analyzing simulation results. You can either export different formats for external processing or use the integrated functions for visualization of model results.
Analysis declarations are shown in a model tree.
SeSAm offers support for online and offline analysis. Analysis items are specified by a name, a calculation rule, and one of the following types:
- a) To File - This type is for offline analysis. The data is saved as a character-separated list (.csv) into a file. The standard separating character is the semicolon (;).
- b) Table - This online analysis is similar to the above, the values are Not saved to a file but shown in a table at runtime.
- c) Block - Numeric values are presented in form of a block chart. You can specify a color for each analysis item.
- d) Series - Numeric values are presented in form of a series chart. You can specify a color for each analysis item.
5) Distribution of simulation runs in your local area network (LAN) --
If you do experiments with many simulation runs and different starting parameters, SeSAm offers the possibility to distribute simulation runs within a local network.
Simulation results are saved to the hard drive and can be collected after the remote simulations are finished.
System Requirements
Contact manufacturer.
Manufacturer
- Institute for Artificial Intelligence and Computer Science
- University of Würzburg
- Am Hubland
- D 97074 Würzburg
- Germany
Manufacturer Web Site SeSAm
Price Contact manufacturer.
G6G Abstract Number 20450
G6G Manufacturer Number 104078