This volume presents a collection of refereed papers reflecting the state of the art in the area of over-constrained systems. Besides 11 revised full papers, selected from the 24 submissions to the OCS workshop held in conjunction with the First International Conference on Principles and Practice of Constraint Programming, CP '95, held in Marseilles in September 1995, the book includes three comprehensive background papers of central importance for the workshop papers and the whole field. Also included is an (...) introduction by one of the volume editors together with a bibliography listing 243 entries. All in all this is a very useful reference book relevant for all researchers and practitioners interested in hierarchical, partial, and over-constrained systems. (shrink)

According to best systems accounts, laws of nature are generalizations in the best systematization of particular matters of fact. Metrics such as simplicity and strength determine which systematization is best, but these are notoriously language relative. For this reason, David Lewis proposed a constraint on languages of inquiry: all predicates must be natural. This constraint is sometimes interpreted as requiring us to know which natural properties are instantiated in our world prior to scientific theorizing. I argue that (...) this interpretation is incorrect. I provide a better interpretation and show how it undercuts an influential epistemological objection to Lewis's best systems account of laws due to Bas van Fraassen. (shrink)

Different cell lineages growing in microgravity undergo a spontaneous transition leading to the emergence of two distinct phenotypes. By returning these populations in a normal gravitational field, the two phenotypes collapse, recovering their original configuration. In this review, we hypothesize that, once the gravitational constraint is removed, the system freely explores its phenotypic space, while, when in a gravitational field, cells are “constrained” to adopt only one favored configuration. We suggest that the genome allows for a wide range of (...) “possibilities” but it is unable per se to choose among them: the emergence of a specific phenotype is enabled by physical constraints that drive the system toward a preferred solution. These findings may help in understanding how cells and tissues behave in both development and cancer. In microgravity, cells undergo spontaneous and reversible transitions between different phenotypes. In the absence of physical constraints, living systems could yield bi-stable decisions. On the contrary, physical ‘boundaries’ constrain cells to acquire only a specific configuration by selecting and shaping different gene expression patterns provided by the intrinsic genetic stochasticity. (shrink)

A new conceptual framework is proposed to situate and integrate the parallel theories of Turchin, Powers, Campbell and Simon. A system is defined as a constraint on variety. This entails a 2 × 2 × 2 classification scheme for “higher‐order” systems, using the dimensions of constraint, (static) variety, and (dynamic) variation. The scheme distinguishes two classes of metasystems from supersystems and other types of emergent phenomena. Metasystems are defined as constrained variations of constrained variety. Control is characterized (...) as a constraint exerted by a separate system. The emergence of hierarchical systems is motivated by evolutionary principles. The positive feedback between variety and constraint, which underlies the “branching growth of the penultimate level,” leads to the interpretation of metasystem transitions as phases of accelerated change in a continuous evolutionary progression toward increasing variety. The most important MSTs in the history of evolution are reinterpreted in this framework: mechanical motion, dissipative structuration, life, multicellular differentiation, sexuality, simple reflex, complex reflex, associating, thinking, metarationality and social interaction. (shrink)

Complexity arises from interaction dynamics, but its forms are co-determined by the operative constraints within which the dynamics are expressed. The basic interaction dynamics underlying complex systems is mostly well understood. The formation and operation of constraints is often not, and oftener under appreciated. The attempt to reduce constraints to basic interaction fails in key cases. The overall aim of this paper is to highlight the key role played by constraints in shaping the field of complex systems. Following (...) an introduction to constraints, the paper develops the roles of constraints in specifying forms of complexity and illustrates the roles of constraints in formulating the fundamental challenges to understanding posed by complex systems. (shrink)

Origin of a dissipative structure in a chemical dynamic system: occurs under the following constraints: 1) Affinity must be high. (The system must be far from equilibrium.); 2) There must be an auto-catalytic process; 3) A process that reduces the concentration of the auto-catalyst must operate; 4) The relevant parameters (rate constants, etc.) must lie in a range corresponding to a limit cycle trajectory. That is, there must be closure of the network of reaction such that a state sufficiently close (...) to the prior condition is achieved at the corresponding part of each oscillation. If these constraints continue to be met interactions of the system with the rest of the world are quite different in the presence of the dissipative structure than they would be in the absence of that self-organized coherence. Closure of a network of relationships which gives rise to a dissipative structure is a 'unit-determining' feature. The effects of the structure as a whole are the resultants of the effects of the components, but the concentrations of the components at any instant are effects of the closure of the limit cycle. This is an influence on components arising from what those components constitute --- downward causation. (shrink)

Animal health is key to successful livestock production in developing countries. The development and delivery of vaccines against major epidemic diseases is one component of improving animal health. This paper presents a case study from Kenya on the production and delivery of a vaccine against Contagious Caprine Pleuropneumonia (CCPP), a major disease of goats. The vaccine, while technically a viable preventative measure against CCPP, has not been well integrated into Kenya's animal health care system. From February through November, 1992, the (...) authors explored the organizational and policy constraints to vaccine production and distribution. Data were obtained through interviews, field visits, and available documents. Results suggest an animal health system that 1) does not have a well developed infrastructure to track, identify, and control animal diseases; 2) tends to place greater emphasis on cattle, rather than small ruminant health care; and 3) is not well linked in terms of research, development, and extension services. (shrink)

Modern integrative systems biology defines itself by the complexity of the problems it takes on through computational modeling and simulation. However in integrative systems biology computers do not solve problems alone. Problem solving depends as ever on human cognitive resources. Current philosophical accounts hint at their importance, but it remains to be understood what roles human cognition plays in computational modeling. In this paper we focus on practices through which modelers in systems biology use computational simulation and (...) other tools to handle the cognitive complexity of their modeling problems so as to be able to make significant contributions to understanding, intervening in, and controlling complex biological systems. We thus show how cognition, especially processes of simulative mental modeling, is implicated centrally in processes of model-building. At the same time we suggest how the representational choices of what to model in systems biology are limited or constrained as a result. Such constraints help us both understand and rationalize the restricted form that problem solving takes in the field and why its results do not always measure up to expectations. (shrink)

It is widely mooted that a plausible computational cognitive model should involve both symbolic and connectionist components. However, sound principles for combining these components within a hybrid system are currently lacking; the design of such systems is oftenad hoc. In an attempt to ameliorate this we provide a framework of types of hybrid systems and constraints therein, within which to explore the issues. In particular, we suggest the use of system independent constraints, whose source lies in general considerations (...) about cognitive systems, rather than in particular technological or task-based considerations. We illustrate this through a detailed examination of an interruptibility constraint: handling interruptions is a fundamental facet of cognition in a dynamic world. Aspects of interruptions are delineated, as are their precise expression in symbolic and connectionist systems. We illustrate the interaction of the various constraints from interruptibility in the different types of hybrid systems. The picture that emerges of the relationship between the connectionist and the symbolic within a hybrid system provides for sufficient flexibility and complexity to suggest interesting general implications for cognition, thus vindicating the utility of the framework. (shrink)

This paper approaches the question of how to describe the higher-level internal structures and representations of cognitive systems across various kinds of nonhuman (neuro)cognitive systems. While much research in cognitive (neuro)science and comparative cognition is dedicated to the exploration of the (neuro)cognitive mechanisms and processes with a focus on brain-behavior relations across different non-human species, not much has been done to connect (neuro)cognitive mechanisms and processes and the associated behaviors to plausible higher-level structures and representations of distinct kinds (...) of cognitive systems in non-humans. Although the study of (neuro)cognitive mechanisms and processes can certainly be revealing, (neuro)cognitive mechanisms and processes are underspecified with respect to internal structures and representations of non-human cognitive systems because multiple such mechanisms can target, or be mapped onto, the same internal structure or vice versa. This paper outlines a biosemiotic approach to this linking problem in order to bridge the gap between functions of (neuro)cognitive systems in different species and the higher-level cognitive structures and representations. It is contended that the higher-level internal structures and representations of various cognitive systems are biosemiotic constraints on the (biological) functions of (neuro)cognitive systems that serve to restrict the range of functions (neuro)cognitive systems have or are selected for. This turns out to have implications for issues on the convergent evolution of cognitive traits. (shrink)

The consensus problem is presented for the switched multiagent system, where the MAS is switched between continuous- and discrete-time systems with relative state constraints. With some standard assumptions, we obtain the fact that the switched MAS with relative state constraints can achieve consensus under both fixed undirected graphs and switching undirected graphs. Furthermore, based on the absolute average value of initial states, we propose sufficient conditions for consensus of the switched MAS. The challenge of this study is that relative (...) state constraints are considered, which will make the consensus problem much more complex. One of the main contributions is that, for the switched MAS with relative state constraints, we explore the solvability of the consensus problem. Finally, we present two simulation examples to show the effectiveness of the results. (shrink)

Contemporary complexity theory has been instrumental in providing novel rigorous definitions for some classic philosophical concepts, including emergence. In an attempt to provide an account of emergence that is consistent with complexity and dynamical systems theory, several authors have turned to the notion of constraints on state transitions. Drawing on complexity theory directly, this paper builds on those accounts, further developing the constraint-based interpretation of emergence and arguing that such accounts recover many of the features of more traditional (...) accounts. We show that the constraint-based account of emergence also leads naturally into a meaningful definition of self-organization, another concept that has received increasing attention recently. Along the way, we distinguish between order and organization, two concepts which are frequently conflated. Finally, we consider possibilities for future research in the philosophy of complex systems, as well as applications of the distinctions made in this paper. (shrink)

Open peer commentary on the article “Circularity and the Micro-Macro-Difference” by Manfred Füllsack. Upshot: The target article defends the fundamental role of circularity for systems sciences and the necessity to develop a conceptual and methodological approach to it. The concept of circularity, however, is multifarious, and two of the main challenges in this respect are to provide distinctions between different forms of circularities and explore in detail the roles they play in organizations. This commentary provides some suggestions in this (...) direction with the aim to supplement the perspective presented in the target article with some insights from theoretical biology. (shrink)

There is a systemic condition inherent in contemporary markets that compel managers not to pursue more morally preferable initiatives if those initiatives will require actions that conflict with profit maximization. Normative arguments for implementing morally preferable practices within the existing system fail because they are insufficient to counter-act the systemic conditions affecting decision-making that is focused on maximizing profit as the primary operational value. To overcome this constraint we must elevate a more normatively preferable value, ‚ideal environmental sustainability,’ to (...) the level of being the primary filtering value through which other competing values are evaluated, prioritized, and implemented. In order for this to occur in practice, a change must be made relative to the laws, rules, and regulations that define and guide the market. This can be done by suitably defining the epistemic constraint of impartiality utilizing Rawls’ notion of a ‚veil of ignorance’ as a heuristic device. (shrink)

Living systems employ several mechanisms and behaviors to achieve robustness and maintain themselves under changing internal and external conditions. Regulation stands out from them as a specific form of higher-order control, exerted over the basic regime responsible for the production and maintenance of the organism, and provides the system with the capacity to act on its own constitutive dynamics. It consists in the capability to selectively shift between different available regimes of self-production and self-maintenance in response to specific signals (...) and perturbations, due to the action of a dedicated subsystem which is operationally distinct from the regulated ones. The role of regulation, however, is not exhausted by its contribution to maintain a living system’s viability. While enhancing robustness, regulatory mechanisms play a fundamental role in the realization of an autonomous biological organization. Specifically, they are at the basis of the remarkable integration of biological systems, insofar as they coordinate and modulate the activity of distinct functional subsystems. Moreover, by implementing complex and hierarchically organized control architectures, they allow for an increase in structural and organizational complexity while minimizing fragility. Finally, they endow living systems, from their most basic unicellular instances, with the capability to control their own internal dynamics to adaptively respond to specific features of their interaction with the environment, thus providing the basis for the emergence of minimal forms of cognition. (shrink)

In this study, an accurate convergence time of the supertwisting algorithm is proposed to build up a framework for nonaffine nonlinear systems’ finite-time control. The convergence time of the STA is provided by calculating the solution of a differential equation instead of constructing Lyapunov function. Therefore, precise convergence time is presented instead of estimation of the upper bound of the algorithm’s reaching time. Regardless of affine or nonaffine nonlinear systems, supertwisting control provides a general solution based on virtual (...) control law skill ensuring the output of the systems converges to the origin point at exact time. Benchmark tests are simulated to demonstrate the effectiveness and efficiency of the algorithm. (shrink)

An adaptive neural network control problem is addressed for a class of nonlinear hydraulic servo-systems with time-varying state constraints. In view of the low precision problem of the traditional hydraulic servo-system which is caused by the tracking errors surpassing appropriate bound, the previous works have shown that the constraint for the system is a good way to solve the low precision problem. Meanwhile, compared with constant constraints, the time-varying state constraints are more general in the actual systems. (...) Therefore, when the states of the system are forced to obey bounded time-varying constraint conditions, the high precision tracking performance of the system can be easily realized. In order to achieve this goal, the time-varying barrier Lyapunov function is used to prevent the states from violating time-varying constraints. By the backstepping design, the adaptive controller will be obtained. A radial basis function neural network is used to estimate the uncertainties. Based on analyzing the stability of the hydraulic servo-system, we show that the error signals are bounded in the compacts sets; the time-varying state constrains are never violated and all singles of the hydraulic servo-system are bounded. The simulation and experimental results show that the tracking accuracy of system is improved and the controller has fast tracking ability and strong robustness. (shrink)

The problem of adaptive finite-time fault-tolerant control and output constraints for a class of uncertain nonlinear half-vehicle active suspension systems are investigated in this work. Markovian variables are used to denote in terms of different random actuators failures. In adaptive backstepping design procedure, barrier Lyapunov functions are adopted to constrain vertical motion and pitch motion to suppress the vibrations. Unknown functions and coefficients are approximated by the neural network. Assisted by the stochastic practical finite-time theory and FTC theory, the (...) proposed controller can ensure systems achieve stability in a finite time. Meanwhile, displacement and pitch angle in systems would not violate their maximum values, which imply both ride comfort and safety have been enhanced. In addition, all the signals in the closed-loop systems can be guaranteed to be semiglobal finite-time stable in probability. The simulation results illustrate the validity of the established scheme. (shrink)

The paper examines constraints and preferences employed by people in learning decision rules from preclassified examples. Results from four experiments with human subjects were analyzed and compared with artificial intelligence (AI) inductive learning programs. The results showed the people's rule inductions tended to emphasize category validity (probability of some property, given a category) more than cue validity (probability that an entity is a member of a category given that it has some property) to a greater extent than did the AI (...) programs. Although the relative proportions of different rule types (e.g., conjunctive vs. disjunctive) changed across experiments, a single process model provided a good account of the data from each study. These observations are used to argue for describing constraints in terms of processes embodied in models rather than in terms of products or outputs. Thus AI induction programs become candidate psychological process models and results from inductive learning experiments can suggest new algorithms. More generally, the results show that human inductive generalizations tend toward greater specificity than would be expected if conceptual simplicity were the key constraint on inductions. This bias toward specificity may be due to the fact that this criterion both maximizes inferences that may be drawn from category membership and protects rule induction systems from developing over‐generalizations. (shrink)

Fundamental limitations constraining the application of emergence to formulations of conscious mental states are explored within the paradigm of classical science. This paradigm includes standard interpretations of functionalism, computationalism and complex systems theories of mind -- theories which are ultimately justified by an appeal to emergentist principles. We define a distinction between extrinsic and intrinsic accounts of emergent conscious states, and examine the prospects for both. Extrinsic accounts are subject to relativities with respect to external observers that must be (...) resolved if the ontological character of conscious states is to be preserved. While this can, in some cases, be accomplished by imposing an appropriate invariance, no such strategy exists in the case of relativity with respect to boundary without absurd consequences. If, on the other hand, conscious states require intrinsic definition, a specification of the system boundary must be explicitly available if the conscious ontology is to be uniquely specified. Even minimal information requirements make this incompatible with locality constraints. We investigate what progress can be made in overcoming these obstacles by relaxing various assumptions. (shrink)

In this paper, we mainly investigate the coordinated tracking control issues of multiple Euler–Lagrange systems considering constant communication delays and output constraints. Firstly, we devise a distributed observer to ensure that every agent can get the information of the virtual leader. In order to handle uncertain problems, the neural network technique is adopted to estimate the unknown dynamics. Then, we utilize an asymmetric barrier Lyapunov function in the control design to guarantee the output errors satisfy the time-varying output constraints. (...) Two distributed adaptive coordinated control schemes are proposed to guarantee that the followers can track the leader accurately. The first scheme makes the tracking errors between followers and leader be uniformly ultimately bounded, and the second scheme further improves the tracking accuracy. Finally, we utilize a group of manipulator networks simulation experiments to verify the validity of the proposed distributed control laws. (shrink)

This paper proposes an adaptive neural network control approach for a direct-current system with full state constraints. To guarantee that state constraints always remain in the asymmetric time-varying constraint regions, the asymmetric time-varying Barrier Lyapunov Function is employed to structure an adaptive NN controller. As we all know that the constant constraint is only a special case of the time-varying constraint, hence, the proposed control method is more general for dealing with constraint problem as compared with (...) the existing works on DC systems. As far as we know, this system is the first studied situations with time-varying constraints. Using Lyapunov analysis, all signals in the closed-loop system are proved to be bounded and the constraints are not violated. In this paper, the effectiveness of the control method is demonstrated by simulation results. (shrink)

The stabilization problem of a macroeconomic dynamical system is considered in this paper. The main features of this system are that the system uncertainties may be unknown functions of state and time but with known bounds. Furthermore, the control inputs are subject to constraints, which is a salient feature in an economic control problem. To ensure that the controls are within the specified boundaries, in our control design procedure, a creative diffeomorphism, which converts bounded controls into unbounded corresponding signals by (...) choosing an appropriate transformation function, is proposed. For the uncertain system, a deterministic robust control is designed to render the practical stability: uniform boundedness and uniform ultimate boundedness. The range of the input bounds is related to the uncertainties and can be designed according to the actual situation. Numerical simulations are performed to verify the effectiveness of the stabilization policy. (shrink)

The problem of finite-time tracking control is discussed for a class of uncertain nonstrict-feedback time-varying state delay nonlinear systems with full-state constraints and unmodeled dynamics. Different from traditional finite-control methods, a C 1 smooth finite-time adaptive control framework is introduced by employing a smooth switch between the fractional and cubic form state feedback, so that the desired fast finite-time control performance can be guaranteed. By constructing appropriate Lyapunov-Krasovskii functionals, the uncertain terms produced by time-varying state delays are compensated for (...) and unmodeled dynamics is coped with by introducing a dynamical signal. In order to avoid the inherent problem of “complexity of explosion” in the backstepping-design process, the DSC technology with a novel nonlinear filter is introduced to simplify the structure of the controller. Furthermore, the results show that all the internal error signals are driven to converge into small regions in a finite time, and the full-state constraints are not violated. Simulation results verify the effectiveness of the proposed method. (shrink)

This article investigates an adaptive neural network control algorithm for marine surface vessels with time-varying output constraints and unknown external disturbances. The nonlinear state-dependent transformation is introduced to eliminate the feasibility conditions of virtual controller. Moreover, the barrier Lyapunov function is used to achieve time-varying output constraints. As an important approximation tool, the NN is employed to approximate uncertain and continuous functions. Subsequently, the disturbance observer is structured to observe time-varying constraints and unknown external disturbances. The novel strategy can guarantee (...) that all signals in the closed-loop system are semiglobally uniformly ultimately bounded. Finally, the simulation results verify the benefit of the proposed method. (shrink)

We prove an exponential lower bound on the size of proofs in the proof system operating with ordered binary decision diagrams introduced by Atserias, Kolaitis and Vardi [2]. In fact, the lower bound applies to semantic derivations operating with sets defined by OBDDs. We do not assume any particular format of proofs or ordering of variables, the hard formulas are in CNF. We utilize (somewhat indirectly) feasible interpolation. We define a proof system combining resolution and the OBDD proof system.

Living systems employ several mechanisms and behaviors to achieve robustness and maintain themselves under changing internal and external conditions. Regulation stands out from them as a specific form of higher-order control, exerted over the basic regime responsible for the production and maintenance of the organism, and provides the system with the capacity to act on its own constitutive dynamics. It consists in the capability to selectively shift between different available regimes of self-production and self-maintenance in response to specific signals (...) and perturbations, due to the action of a dedicated subsystem which is operationally distinct from the regulated ones. The role of regulation, however, is not exhausted by its contribution to maintain a living system’s viability. While enhancing robustness, regulatory mechanisms play a fundamental role in the realization of an autonomous biological organization. Specifically, they are at the basis of the remarkable integration of biological systems, insofar as they coordinate and modulate the activity of distinct functional subsystems. Moreover, by implementing complex and hierarchically organized control architectures, they allow for an increase in structural and organizational complexity while minimizing fragility. Finally, they endow living systems, from their most basic unicellular instances, with the capability to control their own internal dynamics to adaptively respond to specific features of their interaction with the environment, thus providing the basis for the emergence of minimal forms of cognition. (shrink)

In this paper, an adaptive sliding mode control method based on neural networks is presented for a class of manipulator systems. The main characteristic of the discussed system is that the output variable is required to keep within a constraint set. In order to ensure that the system output meets the time-varying constraint condition, the asymmetric barrier Lyapunov function is selected in the design process. According to Lyapunov stability theory, the stability of the closed-loop system is analyzed. (...) It is demonstrated that all signals in the resulted system are bounded, the tracking error converges to a small compact set, and the system output limits in its constrained set. Finally, the simulation example is used to show the effectiveness of the presented control strategy. (shrink)

This paper focuses on studying containment control problem with switching communication graphs of continuous-time heterogeneous multiagent systems where the control inputs are constrained in a nonconvex set. A nonlinear projection algorithm is proposed to address the problem. We discuss the stability and containment control of the system with switching topologies and nonconvex control input constraints under three different conditions. It is shown that all agents converge to the convex hull of the given leaders ultimately while staying in the nonconvex (...) set under the premise that at least one directed path from leaders to the agents exists in each bounded time interval. Finally, the validity of the results obtained in this paper is verified by simulation. (shrink)

With the rapid development of urban economy and the acceleration of urbanization, the demand for urban traffic is increasing rapidly. The single traffic-oriented planning does not take into account the requirements of traffic development on resources and the impact on the environment. The traffic construction of most cities can not fully meet the standard of ecotype. In this paper, the vehicle distribution route optimization problem under multiresource constraints such as vehicle energy capacity and vehicle loading capacity is studied, and the (...) static and dynamic models of distribution route optimization under multiresource constraints are established. In the static distribution route optimization model, the distribution route optimization problem with subloops is solved by modifying the network structure and adding virtual resource points. In the dynamic model, the spatiotemporal network model is used to avoid the generation of subloops, and the description of the vehicle distribution route planning problem is more intuitive and accurate. The model enriches the vehicle distribution route selection scheme at the cost of expanding the model scale. And it can solve the time when the vehicle arrives and leaves the customer point. This paper provides a good countermeasure for solving the environmental and social problems in the transportation system under the condition of resource constraints. (shrink)

Open peer commentary on the article “Homeostats for the 21st Century? Simulating Ashby Simulating the Brain” by Stefano Franchi. Upshot: The target article has addressed core concepts of Ashby’s generalized homeostasis thesis as well as its relevance to building complex artificial systems. In this commentary, I discuss Ashby-inspired approaches to designing for ultrastable behaviour in robots and the extent to which complex adaptive behaviour can be underdetermined by heteronomous constraints.

In a previous paper we developed a general theory of input/output logics. These are operations resembling inference, but where inputs need not be included among outputs, and outputs need not be reusable as inputs. In the present paper we study what happens when they are constrained to render output consistent with input. This is of interest for deontic logic, where it provides a manner of handling contrary-to-duty obligations. Our procedure is to constrain the set of generators of the input/output system, (...) considering only the maximal subsets that do not yield output conflicting with a given input. When inputs are authorised to reappear as outputs, both maxichoice revision in the sense of Alchourr6n/Makinson and the default logic of Poole emerge as special cases, and there is a close relation with Reiter default logic. However, our focus is on the general case where inputs need not be outputs. We show in what contexts the consistency of input with output may be reduced to its consistency with a truth-functional combination of components of generators, and under what conditions constrained output may be obtained by a derivation that is constrained at every step. (shrink)

This paper addresses the sliding mode control problem for a class of networked control systems with long time delay and consecutive packet dropout. A new modeling method is proposed, through which time delay and packet dropout are modeled in a unified model described by one Markov chain. To avoid the chattering problem of classic reaching law, a new chattering-free reaching law is proposed. Then with a focus on the problem that controller-actuator channel network condition cannot be foreseen by the (...) controller, a new compensation strategy is proposed, which can effectively compensate for the effect of time delay and packet dropout in controller-actuator channel. Finally, a simulation example is presented to demonstrate the effectiveness of the proposed approach. (shrink)

The output feedback controller is designed for a class of stochastic nonlinear systems that satisfy uncertain function growth conditions for the first time. The multivariate function growth condition has greatly relaxed the restrictions on the drift and diffusion terms in the original stochastic nonlinear system. Here, we cleverly handle the problem of uncertain functions in the scaling process through the function maxima theory so that the Ito differential system can achieve output stabilization through Lyapunov function design and the solution (...) of stochastic nonlinear system objects satisfies the existence of uniqueness, ensuring that the system is globally asymptotically stable in the sense of probability. Furthermore, it is concluded that the system is inversely optimally stable in the sense of probability. Finally, we apply the theoretical results to the practical subsea intelligent electroexecution robot control system and obtain good results. (shrink)

Several articles have recently appeared arguing that there really are no viable alternatives to mechanistic explanation in the biological sciences (Kaplan and Bechtel; Kaplan and Craver). We argue that mechanistic explanation is defined by localization and decomposition. We argue further that systems neuroscience contains explanations that violate both localization and decomposition. We conclude that the mechanistic model of explanation needs to either stretch to now include explanations wherein localization or decomposition fail or acknowledge that there are counterexamples to mechanistic (...) explanation in the biological sciences. (shrink)

In the future systems of ambient intelligence will include decision support systems that will automate the process of discrimination among people that seek entry into environments and to engage in search of the opportunities that are available there. This article argues that these systems must be subject to active and continuous assessment and regulation because of the ways in which they are likely to contribute to economic and social inequality. This regulatory constraint must involve limitations on (...) the collection and use of information about individuals and groups. The article explores a variety of rationales or justifications for establishing these limits. It emphasizes the unintended consequences that flow from the use of these systems as the most compelling rationale. (shrink)