Since many areas, such as biology or discrete-event systems, are naturally described in continuous time, we present a model based on a Markov jumpprocessmodulating asubordinated diffusionprocess. Weprovidetheexact evolution equations fortheprior andposterior marginal densities, thedirect solutions of which are however computationally intractable.

In this paper, we solve the complete coordination problem of robotic fiber positioners using supervisory control theory. In particular, we model positioners and their behavioral specifications as discrete-event systems by the discretization of their motion spaces. We synthesize a coordination supervisor associated with a specific set of positioners. In particular, the coordination supervisor includes the solutions to the complete coordination problem of its corresponding positioners. Then, we use the backtracking forcibility technique of supervisory control theory to present an algorithm based on a completeness condition to solve the coordination problem similar to a reconfiguration problem. We illustrate the functionality of our method using an example.

Because perception-action systems are necessarily constrained by the physics of time and space, robotocists often assume they are best described using differential equations, a language that is specialized for describing the evolution of variables that represent physical quantities. However, when it comes to decision making, where the representations involved refer to goals, strategies, and preferences, AI offers a diverse range of formalisms to the modeler. However, the relationship between these two levels of representation--signal and symbol--are not well understood. If we are to achieve success in modeling intelligent physical agents, robotics and AI must reach a new consensus on how to integrate perception-action systems with systems designed for abstract reasoning. All our major AI laboratories had research programs in robotics in the late 1960s and early 1970s.

Moreover, all of the of a system and, after detection, to determine the location faults that occurred within the (possibly extended) time interval and/or the type of system faults that caused the abnormal during which the system has been observed must be behaviour. A diagnosis hypothesis indicates which fault(s) accounted for in the diagnosis. Considering again the diagnosis occurred in the system, and the diagnosis is the set of alternative of a car, for each component we could be interested hypotheses that explain (i.e., are compatible) with in knowing whether a fault has occurred to it during the last the observed system behaviour. In this paper, we focus on week; in such a case, it is difficult to perform a drastic abstraction Model-Based Diagnosis (MBD) of Discrete-Event Systems of the model without losing any precision in the (DESs, see (Cassandras and Lafortune 1999)), where the diagnosis discrimination among different hypotheses. is computed by comparing a complete DES model In this article, we study a novel approach to reduce the of the system behaviour with a (partial) observation of the complexity of DES diagnosis, based on a reformulation of actual system behaviour (Sampath et al. 1995).

Because perception-action systems are necessarily constrained by the physics of time and space, robotocists often assume they are best described using differential equations, a language that is specialized for describing the evolution of variables that represent physical quantities. However, when it comes to decision making, where the representations involved refer to goals, strategies, and preferences, AI offers a diverse range of formalisms to the modeler. However, the relationship between these two levels of representation -- signal and symbol -- are not well understood. If we are to achieve success in modeling intelligent physical agents, robotics and AI must reach a new consensus on how to integrate perception-action systems with systems designed for abstract reasoning.

Observations play a major role in diagnosis. The nature of an observation varies according to the class of the considered system. In static systems, an observation is the value of a variable at a single time point. In dynamic continuous systems, such a value is observed over a time interval. In discrete-event systems, an observation consists of a sequence of temporally ordered events. In any case, what is observed is assumed not to be ambiguous.