In the optimization of decision diagrams, variable reordering approaches are often used to minimize the number of nodes. However, such approaches are less effective for analysis of multi-state systems given by monotone structure functions. Thus, in this paper, we propose algorithms to minimize the number of edges in an edge-valued multi-valued decision diagram (EVMDD) for fast analysis of multi-state systems. The proposed algorithms minimize the number of edges by grouping multi-valued variables into larger-valued variables. By grouping multi-valued variables, we can reduce the number of nodes as well. To show the effectiveness of the proposed algorithms, we compare the proposed algorithms with conventional optimization algorithms based on a variable reordering approach. Experimental results show that the proposed algorithms reduce the number of edges by up to 15% and the number of nodes by up to 47%, compared to the conventional ones. This results in a speed-up of the analysis of multi-state systems by about three times.

The specification of a Multi-Agent System (MAS) involves the identification of a large number of entities and their relationships. This is a non-trivial task that requires managing different views of the system. Many problems concerning this issue originate in the presence of contradictory goals and tasks, inconsistencies, and unexpected behaviours. Such troublesome configurations should be detected and prevented during the development process in order to study alternative ways to cope with them. In this paper, we present methods and tools that support the management of contradictions during the analysis and design of MAS. Contradiction management in MAS has to consider both individual (i.e. agent) and social (i.e. organization) aspects, and their dynamics. Such issues have already been considered in social sciences, and more concretely in the Activity Theory, a social framework for the study of interactions in activity systems. Our approach applies knowledge from Activity Theory in MAS, especially its base of contradiction patterns. That requires a formalization of this social theory in order to be applicable in a software engineering context and its adaptation to agent-oriented methodologies. Then, it will be possible to check the occurrence of contradiction patterns in a MAS specification and provide solutions to those situations. This technique has been validated by implementing an assistant for the INGENIAS Development Kit and has been tested with several case studies. This paper shows part of one of these experiments for a web application.

This paper presents a novel technique for analyzing and designing local communication systems for distributed mobile robotic systems (DMRS). Our goal is to provide an analysis-base guideline for designing local communication systems to efficiently transmit task information to the appropriate robots. In this paper, we propose a layered methodology, i. e. , design from spatial and temporal aspects based on analysis of information diffusion by local communication between robots. The task environment is classified so that each analysis and design is applied in a systematic way. The spatial design gives the optimal communication area for minimizing transmission time for various cooperative tasks. In the temporal design, we derive the information announcing time to avoid excessive information diffusion. The designed local communication is evaluated in comparison with global communication. Finally, we performed simulations and experiments to demonstrate that the analysis and design technique is effective for constructing an efficient local communication system.

We deal here with diagnosis for failures of series/parallel structure system. The conventionals have assumed that the system failure is caused by a single minimal cut set (MCS). The purposes of this paper are to propose a new diagnosis method to identify all MCSs by utilizing the series/parallel structure and repair information without requiring an excessive number of inspections. Moreover, by applying our method to several types of series/parallel structure system, and to system having some kinds of probability distributions, desirable system for our method are persuaded. We evaluate not just the number of inspections but also the cost of diagnosis, and show the condition under which our method is effective.

Electric fishes generate an AC electric field around themselves by the electric organ in the tail. Spatial distortion of the field by nearby objects is detected by an electroreceptor array located an over the body surface to localize the object electrically when other senses such as vision and mechanosense are useless. Each fish has its own 'frequency band' for its electric organ discharges, and jamming of the electrolocation system occurs when two fish with similar discharge frequencies encounter. To avoid janmming, the fish shift their discharge frequencies in appropriate directions. A computational algorithm for this electrical behavior and its neuronal implementation by the brain have been discovered. The design features of the system, however, are rather complex for this simple behavior and cannot be readily explained by functional optimization processes during evolution. To gain insights into the origin of the design features, two independently evolved electric fish species which perform the same behavior are compared. Complex features of the neuronal computation may be explained by the evolutionary history of neuronal elements.

In this paper a priori estimation method is presented for calculating solution of convex optimization problems (COP) with some equality and/or inequality constraints by so-called Newton type homotopy method. The homotopy method is known as an efficient algorithm which can always calculate solution of nonlinear equations under a certain mild condition. Although, in general, it is difficult to estimate a priori computational complexity of calculating solution by the homotopy method. In the presented papers, a sufficient condition is considered for linear homotopy, under which an upper bound of the complexity can be estimated a priori. For the condition it is seen that Urabe type convergence theorem plays an important role. In this paper, by introducing the results, it is shown that under a certain condition a global minimum of COP can be always calculated, and that computational complexity of the calculation can be a priori estimated. Suitability of the estimation for analysing COP is also discussed.

This paper proposes a methodology for fine evaluation of the uncertain behaviors of systems affected by any fluctuation of internal structures and internal parameters, by the use of a new concept on the fuzzy mapping. For a uniformly convex real Banach space X and Y, a fuzzy mapping G is introduced as the operator by which we can define a bounded closed compact fuzzy set G(x,y) for any (x,y)∈X×Y. An original system is represented by a completely continuous operator f defined on X, for instance, in a form xλ(f(x)) by a continuous operator λ: YX. The nondeterministic fluctuations induced into the original system are represented by a generalized form of the fuzzy mapping equation xGβ (x,f(x)) {ζX|µG(x,f(x))(ζ)β}, in order to give a fine evaluation of the solutions with respect to an arbitrarily–specified β–level. By establishing a useful fixed point theorem, the existence and evaluation problems of the "β–level-likely" solutions are discussed for this fuzzy mapping equaion. The theory developed here for the fluctuation problems is applied to the fine estimation of not only the uncertain behaviors of system–fluctuations but also the validity of system–models and -simulations with uncertain properties.

An estimation method of region is presented, in which a solution path of the so-called Newton type homotopy equation in guaranteed to exist, it is applied to a certain class of uniquely solvable nonlinear equations. The region can be estimated a posteriori, and its upper bound also can be estimated a priori.

A priori estimation is presented for a computational complexity of the homotopy method applying to a certain class of strongly monotone nonlinear equations. In the present papers, a condition is presented for a certain class of uniquely solvable equations, under which an upper bound of a computational complexity of the Newton type homotopy method can be a priori estimated. In this paper, a condition is considered in a case of linear homotopy equations including the Newton type homotopy equations. In the first place, the homotopy algorithm based on the simplified Newton method is introduced. Then by using Urabe type theorem, which gives a sufficient condition guaranteeing the convergence of the simplified Newton method, a condition is presented under which an upper bound of a computational complexity of the algorithm can be a priori estimated, when it is applied to a certain class of strongly monotone nonlinear equations. The presented condition is demonstrated by numerical experiments.