Towards a formal semantics of event-based multi-agent simulations

Abstract. The aim of this paper is to define a non-ambiguous operational semantics for event-based multi-agent modeling and simulation, applied to complex systems. A number of features common to most multi-agent systems have been retained: 1) agent proactive as well as reactive behavior, 2) concurrency: events can arrive simultaneously to an agent, an environment or any simulated entity and the actual change only depends on the target according to the influence/reaction paradigm [1], 3) instantaneity: if reaction takes time, perception as well as information diffusion is instantaneous and should be processed separately, 4) structure dynamics: the interaction structure (who is talking to whom) changes over time, and the agents as well as any simulated entity may be created or destroyed in the course of the simulation. For each of these features, a solution inspired by the work on DEVS (Discrete EVent Systems, [2]) is proposed. Proactive/reactive behavior is naturally taken into account by DEVS. Concurrency is dealt with using //−DEVS (in [2]), a variant of the pure DEVS. Instantaneity is managed by distinguishing the physical events producing state transitions and the logical events realizing only perception and information diffusion. The structure dynamics is achieved by using a variant of ρ-DEVS (cf. [3]) where the expressiveness allows to manage hierarchical structures. The operational semantics is given as abstract algorithms and the expressive power of this formalism is illustrated on a simple example.

Discrete event multi-level models for systems biology

Abstract. Diverse modeling and simulation methods are being applied in the area of Systems Biology. Most models in Systems Biology can easily be located within the space that is spanned by three dimensions of modeling: continuous and discrete; quantitative and qualitative; stochastic and deterministic. These dimensions are not entirely independent nor are they exclusive. Many modeling approaches are hybrid as they combine continuous and discrete, quantitative and qualitative, stochastic and deterministic aspects. Another important aspect for the distinction of modeling approaches is at which level a model describes a system: is it at the “macro ” level, at the “micro ” level, or at multiple levels of organization. Although multi-level models can be located anywhere in the space spanned by the three dimensions of modeling and simulation, clustering tendencies can be observed whose implications are discussed and illustrated by moving from a continuous, deterministic quantitative macro model to a stochastic discrete-event semi-quantitative multi-level model.

Direct controller design and iterative tuning applied to the coupled drives apparatus

V příspěvku je použita přímá metoda návrhu regulátorů VRFT ( Virtual Reference Feedback Tuning) pro iterační způsob návrhu a doladění regulátoru. V práci je využito série experimentů bez omezení na způsob jejich generování pro návrh optimálního regulátoru zvolené struktury bez nutnosti identifikovat řízený proces. Přístup je úspěšně aplikován při návrhu a dolaďování regulátoru pro elektrické zařízení - systém spřažených servomotorů, a to přizpůsobováním požadované doby ustálení řízené veličiny.The paper utilizes the direct method Virtual Reference Feedback Tuning (VRFT) for the iterative way of controller design and fine-tuning. In the work, a series of experiments with no restriction on data generation is used to design an optimal controller of desired structure without the intermediate plant identification step. The approach is shown to be successful for the design and fine-tuning of a controller for the electric device - coupled drives apparatus by means of adjusting a desired settling-time of controlled output