Multi-agent Based Simulation: Where Are the Agents? [chapter]

Alexis Drogoul, Diane Vanbergue, Thomas Meurisse
<span title="">2003</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
This paper is devoted to exploring the relationships between computational agents, as they can be found in multi-agent systems (MAS) or Distributed Artificial Intelligence (DAI), and the different techniques regrouped under the generic appellation "multi-agent based simulation" (MABS). Its main purpose is to show that MABS, despite its name, is in fact rarely based on computational agents. We base our demonstration on an innovative presentation of the methodological process used in the
more &raquo; ... nt of current MABS systems. This presentation relies on the definition of the different roles involved in the design process, and we are able to show that the notion of "agent", although shared at a conceptual level by the different participants, does not imply a systematic use of computational agents in the systems deployed. We then conclude by discussing what the use of computational agents, based on the most interesting research trends in DAI or MAS, might provide MABS with. Alexis Drogoul, Diane Vanbergue, Thomas Meurisse Syntax and Semantics This success is, however, ambiguous. While most of the researchers seem to agree on a common terminology for designating the core multi-agent concepts used in MABS, it appears that this agreement is, at best, syntactic. The semantics associated differ considerably from one model to another, or from one implementation to another. What do the agents described in MANTA [14] have in common with the ones used in Sichman's work [16] ? Nothing beside the label. How come that it is usually impossible to compare two different implementations of the same "specifications" (see, e.g., these papers about Sugarscape [17, 18, 19] ) ? What are the differences between the various platforms available, like Swarm, MadKit, StarLogo, CORMAS, etc. and which one should be chosen given a particular project ? This fuzziness, at the computational level, about what an agent really is can be found in all the other levels required for the design of a simulation. Domain experts (thematicians, as we will call them later) have, at best, a sketchy idea about what is really allowed or not for defining their models. Contrary to numerical simulation, where their knowledge can only be represented by variables and relationships between variables, MABS allows, in theory, for a much wider range of representations : formulae, rules, heuristics, procedures, etc. This wealth is source of confusion for many thematicians and, eventually, disillusion during the course of a project, since only a few of them really know how this knowledge is to be computationally translated and interpreted (it may depend on the platform, the architecture of the MAS, even the language used). As a matter of fact, in most MABS research works, the languages used by thematicians, modelers and computer scientists, while syntactically coherent, often hide important semantic disparities [10, 20] . As long as these discrepancies remain implicit, and do not end up in strong incompatibilities, they can be "masked" with the -unfortunately traditional -help of little "compromises" at the computational level. But the negative side-effect is that there is absolutely no guarantee that what is being designed and implemented corresponds to what has been desired and modeled by the thematician [45] . Furthermore, as far as multi-agent systems are concerned, another consequence of these discrepancies is that, in order to agree upon a shared notion of "agent", the researchers have to make it "weak", i.e. as consensual as possible given their different backgrounds. An "agent" is then described as an "autonomous", "proactive", "interacting" entity, but we will show in this paper that these features, defined at a metaphorical level, do not translate into computational properties. Computational agents as they are defined in DAI or MAS [5] are simply not used in today's MABS. To illustrate this opinion, we will review, in section 2, the main methodological proposals for multi-agent simulation found in the recent literature. We will show that they underestimate most of the difficulties found in building the computational model, which leads us to propose, in section 3, our own methodological framework (more details may also be found in [21] ). We use this framework, in section 4, to detail the models in which the concept of agent is being used and conclude, in section 5, on the benefits that agent-based computational models could provide to MABS. The assumptions and hypotheses found in this paper are based on our ten-years old experience in MABS, which we used, for instance, for simulating biological [14] , physical [22] , social [23] or economic systems [12] .
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1007/3-540-36483-8_1</a> <a target="_blank" rel="external noopener" href="">fatcat:2kak3q327ne53bsvaosdmoizs4</a> </span>
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