Multiple Constraint Satisfaction Research Articles

Four Versions of Transtheoretical Stances, and the Bernese View.

A brief characterization of transtheoretical stances to which existing approaches can be allocated is followed by a description of the "Bernese view", that is, what Klaus Grawe and his colleagues, including the authors of this article have developed: the origins, a model of the multiple constraint satisfaction construction of therapist action, a discussion of psychotherapy integration, the crucial role of supervisors in an integrative multiple constraint satisfaction approach, and a discussion of when and how trainees should be introduced to a transtheoretical stance.

Broad-Angle Multichannel Metagrating Diffusers

We present a semianalytical scheme for the design of broad-angle multichannel metagratings (MGs), sparse periodic arrangements of loaded conducting strips (meta-atoms), embedded in a multilayer printed circuit board (PCB) configuration. By judicious choice of periodicity and angles of incidence, scattering off such an MG can be described via a multiport network, where the input and output ports correspond to different illumination and reflection directions associated with the same set of propagating Floquet–Bloch (FB) modes. Since each of these possible scattering scenarios can be modeled analytically, constraints can be conveniently applied on the modal reflection coefficients (scattering matrix entries) to yield a diffusive response, which, when resolved, produce the required MG geometry. We show that by demanding a symmetric MG configuration, the number of independent S parameters can be dramatically reduced, enabling satisfaction of multiple such constraints using a single sparse MG. Without any full-wave optimization, this procedure results in a fabrication-ready layout of a multichannel MG, enabling retroreflection suppression and diffusive scattering from numerous angles of incidence simultaneously. This concept, verified experimentally via a five-channel MG prototype, offers an innovative solution to both monostatic and bistatic radar cross Section (RCS) reduction, avoiding design and implementation challenges associated with dense metasurfaces used for this purpose.

Processing of Transitivity Alternations and Frequency-Based Accounts in Greek Adult Language

The processing and resolution of syntactically ambiguous structures is accounted for by serial autonomous and multiple constraint satisfaction models differently. We investigated the extent to which frequency affects native speakers’ processing and interpretation of ‘voice (non)-alternating’ anticausative Greek verbs which differ in the availability or lack of voice alternation on the verb when it appears in intransitive structures. The accessibility of interpretations was measured with an online self-paced reading (SPR) task and an offline acceptability judgment (AJ) task addressed to 45 monolingual Greek-speaking adults. In order to investigate whether processing load is affected by statistical records in the parser, we compared empirical data with the frequency of the available readings that these verbs receive in formal and informal registers (ILSP, Web-Based Corpus). The online processing study indicated that the parser is sensitive to morphological cues ((N)ACT voice marking), while semantic factors such as animacy are integrated in subsequent stages of processing. A frequency effect was found in accordance with ‘coarse-grained’ models of sentence processing, while more ‘fine-grained’ models could not be validated with respect to frequency alone. The majority of acceptability judgements attributed to the verbs investigated correlated with the most frequent interpretations of verb forms in intransitive structures in corpora.

TU59. A FRAMEWORK TO IMPROVE POLYGENIC QUESTIONS AND STRENGTHEN INFERENCES IN PSYCHIATRIC GENETIC EPIDEMIOLOGY

Web Service Composition has been studied intensively in the past two decades, considering the many aspects of the problem, like: semantic modeling, general QoS metrics, services state, and much more. In this paper, our focus is on integrating throughput metric with the automation formalism, without any limitation on automation, i.e. allowing full automation with meaningful throughput propagation. The user request and each service in the respiratory are described by their throughput values that are: an extra requirement for the request; and a maximum allowed load for services. In the proposed version, it is possible that to satisfy a request’s throughput, multiple traditional compositions are used concurrently, adding up for the required throughput. This highly increases the problem complexity both in how to model the combining throughput values, and also computationally, as the algorithms are required to search for multiple compositions. However, we also propose an algorithm that reduces the original problem to multiple constraint satisfaction problem instances and can find compositions for requests and considerable-sized repositories, which we prove through a synthetic evaluation.

Throughput-based Service Composition

Web Service Composition has been studied intensively in the past two decades, considering the many aspects of the problem, like: semantic modeling, general QoS metrics, services state, and much more. In this paper, our focus is on integrating throughput metric with the automation formalism, without any limitation on automation, i.e. allowing full automation with meaningful throughput propagation. The user request and each service in the respiratory are described by their throughput values that are: an extra requirement for the request; and a maximum allowed load for services. In the proposed version, it is possible that to satisfy a request’s throughput, multiple traditional compositions are used concurrently, adding up for the required throughput. This highly increases the problem complexity both in how to model the combining throughput values, and also computationally, as the algorithms are required to search for multiple compositions. However, we also propose an algorithm that reduces the original problem to multiple constraint satisfaction problem instances and can find compositions for requests and considerable-sized repositories, which we prove through a synthetic evaluation.

A Nonsmooth Approach to Controller Synthesis for Boolean Specifications

Robotic systems have been proposed as a solution to a wide array of problems, from autonomous warehousing to precision agriculture. Yet these systems typically require satisfaction of multiple constraints, such as collision avoidance and connectivity maintenance, while completing their primary objectives. Many such problems may be decomposed into stability and invariance criteria (e.g., monitor crop patches while avoiding collisions), and this article utilizes Lyapunov and barrier functions to encode stability and invariance, respectively. Barrier functions provide constraint-satisfaction guarantees, and prior results have established a Boolean composition and controller-synthesis framework for these objects via nonsmooth analysis. However, this past work has yet to address Boolean composition of Lyapunov functions directly and does not apply to all Boolean expressions. This article resolves these issues by providing a general method to encode Boolean expressions of Lyapunov or barrier functions. Moreover, this article develops an associated controller-synthesis algorithm that yields discontinuous yet validating controllers with respect to these Boolean expressions. Experimental results show the efficacy of this article in a precision-agriculture scenario, where a robot swarm must visit crop patches while avoiding collisions.

Near-Field Diffraction Imaging from Multiple Detection Planes

We present diffraction imaging results obtained from multiple near-field diffraction constraints. An iterative phase retrieval algorithm was implemented that uses data redundancy achieved by measuring near-field diffraction intensities at various sample-detector distances. The procedure allows for reconstructing the exit surface wave of a sample within a multiple constraint satisfaction framework neither making use of a priori knowledge as enforced in coherent diffraction imaging (CDI) nor exact scanning grid knowledge as required in ptychography. We also investigate the potential of the presented technique to deal with polychromatic radiation as important for potential application in diffraction imaging by means of tabletop EUV and X-ray sources.

Optimized Resource Provisioning Method for Computational Grid

Grid computing is an accumulation of heterogeneous, dynamic resources from multiple administrative areas which are geographically distributed that can be utilized to reach a mutual end. Development of resource provisioning-based scheduling in large-scale distributed environments like grid computing brings in new requirement challenges that are not being believed in traditional distributed computing environments. Computational grid is applying the resources of many systems in a network to a single problem at the same time. Grid scheduling is the method by which work specified by some means is assigned to the resources that complete the work in the environment which cannot fulfill the user requirements considerably. The satisfaction of users while providing the resources might increase the beneficiary level of resource suppliers. Resource scheduling has to satisfy the multiple constraints specified by the user. The option of resource with the satisfaction of multiple constraints is the most tedious process. This trouble is solved by bringing out the particle swarm optimization based heuristic scheduling algorithm which attempts to select the most suitable resource from the set of available resources. The primary parameters that are taken in this work for selecting the most suitable resource are the makespan and cost. The experimental result shows that the proposed method yields optimal scheduling with the atonement of all user requirements.

Why do people disagree with a statement they do not understand? Relations between comprehension and evaluation of a simple assertion

This article presents the results of a quasi-experimental study that examined the relation between the metacomprehension (i.e., understand/do not understand) and evaluative (i.e., agree/disagree) response to a simple one-sentence statement to compare the relative timing in which these two responses are generated in the course of sentence processing. In the study, participants were asked to provide metacomprehension and evaluative judgements to simple one-sentence assertions, and their response times were measured. Two of the main findings are: first, the response time for the evaluative judgement is faster than the response time for the metacomprehension judgement and, second, the faster response time of the evaluative judgement relative to the metacomprehension judgement is more pronounced either when they are not sure about whether they understand a statement or when they feel they do not understand a statement. The findings are analysed in relation to a multiple constraint satisfaction model of sentence comprehension to generate a possible processing model of a simple one-sentence assertion underlying the generation of metacomprehension and evaluative responses.

The efference cascade, consciousness, and its self: naturalizing the first person pivot of action control.

The 20 billion neurons of the neocortex have a mere hundred thousand motor neurons by which to express cortical contents in overt behavior. Implemented through a staggered cortical “efference cascade” originating in the descending axons of layer five pyramidal cells throughout the neocortical expanse, this steep convergence accomplishes final integration for action of cortical information through a system of interconnected subcortical way stations. Coherent and effective action control requires the inclusion of a continually updated joint “global best estimate” of current sensory, motivational, and motor circumstances in this process. I have previously proposed that this running best estimate is extracted from cortical probabilistic preliminaries by a subcortical neural “reality model” implementing our conscious sensory phenomenology. As such it must exhibit first person perspectival organization, suggested to derive from formating requirements of the brain's subsystem for gaze control, with the superior colliculus at its base. Gaze movements provide the leading edge of behavior by capturing targets of engagement prior to contact. The rotation-based geometry of directional gaze movements places their implicit origin inside the head, a location recoverable by cortical probabilistic source reconstruction from the rampant primary sensory variance generated by the incessant play of collicularly triggered gaze movements. At the interface between cortex and colliculus lies the dorsal pulvinar. Its unique long-range inhibitory circuitry may precipitate the brain's global best estimate of its momentary circumstances through multiple constraint satisfaction across its afferents from numerous cortical areas and colliculus. As phenomenal content of our sensory awareness, such a global best estimate would exhibit perspectival organization centered on a purely implicit first person origin, inherently incapable of appearing as a phenomenal content of the sensory space it serves.

Modelling implicatures from modified numerals

It has been argued that comparative and superlative quantifiers (such as “more than” and “at least”) fail to yield scalar implicatures in unembedded declarative contexts (Krifka, 1999; Fox and Hackl, 2006). However, recent experimental work has shown that such implicatures are available, and that these are constrained by considerations of granularity or numeral salience (Cummins et al., 2012). That is, “more than n” triggers a pragmatic upper-bound, the value of which depends on the numeral n. However, Cummins et al. further show that this effect is weakened by prior mention of the numeral n, a finding that they interpret in terms of priming effects. In this paper I discuss a recent theoretical proposal that accommodates these findings by arguing for a model of numerical quantifier usage based on multiple constraint satisfaction. This approach provides a means of accounting for the influence of contextual factors on the speaker's choice of utterance. It also makes predictions as to how a rational hearer should use context in their interpretation of utterances. Here I explore how this can yield the diversity of interpretations exhibited by participants in Cummins et al. (2012). More generally I consider how such an account predicts that a hearer will infer context on the basis of an utterance, and examine how this offers a potential explanation for the previously observed failure of implicature in the domain of modified numerals. I discuss the implications of this for experimental semantic and pragmatic methodologies, with particular reference to the numeral domain.

Particle Swarm Optimization for Multi-constrained Routing in Telecommunication Networks

By our analysis, the QoS routing is the optimization problem under the satisfaction of multiple QoS constraints. The Particles Swarm Optimization (PSO) is an optimization algorithm which has been applied to finding shortest path in the network. However, it might fall into local optimal solution, and is not able to solve the routing based on multiple constraints. To tackle this problem, we propose a new method of solving the multiple constraints routing. This paper firstly sets up a multi constrained QoS routing model and constructs the fitness value function by transforming the QoS constraints with a penalty function. Secondly, the iterative formulas of the original PSO are improved to tailor to non-continuous search space of the routing problem. Finally, the natural selection and mutation ideas of the Genetic Algorithm (GA) are applied to the PSO to improve the convergent performance. The simulation results show that the proposed GA-PSO algorithm can not only successfully solve the multi-constrained QoS routing problem, but also achieves a better effect in the success rate of the searching optimal path.

Ambient intelligent environments and environmental decisions via agent-based systems

This work introduces an alternative approach to designing ambient intelligent environments by using a multi-agent system consisting of agents that represent inhabitants (humans, animals, plants, and objects) of the environment and physical devices (sensors and actuators) that control and monitor the environment. Inhabitants are able to compromise their own needs for the betterment of the environment as a whole. This synergy creates a balance where each inhabitant potentially receives sub-optimal environmental conditions but the environment as a whole achieves a optimal level. This work addresses several issues involving multiple parameter optimization and constraint satisfaction while maintaining the well being and physical structure of the inhabitants of an environment as well as the comfort of multiple human inhabitants sharing the same environment and its resources.

Divide and concur: A general approach to constraint satisfaction

Many difficult computational problems involve the simultaneous satisfaction of multiple constraints that are individually easy to satisfy. These constraints might be derived from measurements (as in tomography or diffractive imaging), interparticle interactions (as in spin glasses), or a combination of sources (as in protein folding). We present a simple geometric framework to express and solve such problems and apply it to two benchmarks. In the first application (3SAT, a Boolean satisfaction problem), the resulting method exhibits similar performance scaling as a leading context-specific algorithm (WALKSAT). In the second application (sphere packing), the method allowed us to find improved solutions to some old and well-studied optimization problems. Based upon its simplicity and observed efficiency, we argue that this framework provides a competitive alternative to stochastic methods such as simulated annealing.

Multiple Constraint Satisfaction in Judging

Multiple Constraint Satisfaction in Judging

Models of emerging contexts in risky and complex decision settings

Key components of the multiple constraint satisfaction framework are explored in a series of experiments set in complex and ambiguous domains. All cases show the prevalence and importance of a purposeful structuring of the information by the participants. The participants gradually generate coherence, even in cases without increasing information. In accordance with multiple constraint satisfaction predictions, the assessments of inferences increasingly spread apart. Also, the correlations between the dependent variable (the decision) and the independent variables, as well as between the independent variables, consistently grow stronger as the participants progress through the decision stages. The information structuring—a gradual simplification of the component structure—is captured as principal components associated with the various decision stages. Neural networks predict the judgments in the various decision stages relatively well. Finally, the role of the ongoing structuring of the underlying information was explored through the application of trained networks to data in other decision stages.

Going Concern Judgments as a Multiple Constraint Satisfaction Process

Key components of the multiple-constraint satisfaction framework are explored in an extensive experiment set in the complex and ambiguous domain of external auditing. The results show the prevalence and importance of a purposeful, albeit unconscious, structuring of the information by both the professional auditors and the auditing students as they gradually generate coherence and sense. In accordance with multiple constraint satisfaction predictions, the assessments of inferences increasingly spread apart. Also, the correlations between the dependent variable (the decision) and the independent variables, as well as between the independent variables, consistently grow stronger as the participants progress through the decision stages. The information structuring - a gradual simplification of the component structure - is captured as principal components associated with the various decision stages. Neural networks capture the judgments in the various decision stages quite well. Finally, the role of the ongoing structuring of the underlying information was explored through the application of trained networks to data originating in other decision stages.

Reaching to grasp with a multi-jointed arm. I. Computational model.

The generation of goal-directed movements requires the solution of many difficult computational problems. Among these are transformations from extrinsic to intrinsic reference frames, specifying solution paths, removing under-specification due to excess degrees of freedom and path multiplicity, constraint satisfaction, and error correction. There are no current motor-control computational models that address these issues in the context of realistic arm movement with redundant degrees of freedom. In this paper, we conjecture there is a geometric stage between sensory input and physical execution. The geometric stage determines movement trajectories independently of forces. It uses a gradient technique that relies on the metric of the space of postures to resolve endpoint path selection, posture-change specification, error correction, and multiple constraint satisfaction on-line without preplanning. The model is instantiated in an arm with seven degrees of freedom that moves in three-dimensional space. Simulated orientation-matching movements are compared with actual human movement data to assess the validity of several of the model's behavioral predictions.

Review of Coherence in thought and action.

PAUL THAGARD Coherence in Thought and Action Cambridge, mA: Massachusetts Institution of Technology Press, 2000, 328 pages (ISBN 0-26220131-3, us$32.95, Hardcover) Reviewed by WILLIAM E. SMYTHE This volume is a comprehensive presentation of the latest work of Paul Thagard and his research group on coherence theory, an area of inquiry to which the author has made ground-breaking contributions for over a decade. Coherence theory is a peculiar synthesis of philosophy and cognitive science that approaches problems in terms of the satisfaction of multiple constraints within networks of highly interconnected elements. The basic ideas of coherence theory would seem to have broad appeal across a number of areas of contemporary thought. They clearly resonate with the meaning holism of Quine's two dogmas of empiricism in philosophy, with notions of distributed cognition and constraint satisfaction models of decision-making in human cognition, with the current popularity of neural network and connectionist models in the cognitive and neurosciences, and, more generally, with the growing interest in nonlinear dynamics and related phenomena across a wide range of disciplines. The main aim of the present work is to extend the reach of coherence theory beyond its usual applications in cognition and epistemology to questions of ontology, ethics, politics, emotion, social consensus, and probabilistic reasoning. Thagard makes a persuasive if not wholly convincing argument that all of these domains can be thought about in roughly similar ways. At the heart of Thagard's main argument is a surprisingly simple conceptual structure. One begins with a set of elements - propositional and other types of representations - that are linked via numerically weighted relations of coherence (positive constraints) and incoherence (negative constraints). The goal of coherence-based reasoning is to partition the elements into two disjoint sets - accepted and rejected elements - in such a way that overall coherence within each set is maximized. Connectionist algorithms can be applied to compute coherence in an iterative fashion for any completely specified network of elements. The underlying simplicity of this explanatory framework is at first striking, given the wide range of problems to which Thagard subsequently applies it. This initial impression of simplicity quickly evaporates, however, as one reads beyond the introductory chapters. Thagard's basic conceptual structure requires substantial embellishment to deal with various specific issues and areas of application. Thus, we learn that epistemological issues require one to distinguish among five distinct types of coherence: explanatory, analogical, deductive, perceptual, and conceptual. Ethical reasoning adds considerations of deliberative coherence. Understanding emotion requires supplementing relations of cognitive coherence with valence relations and introduces considerations of metacoherence. Consensus formation is based, not just on coherence, but on the vicissitudes of communication and information exchange. Then there is the matter of probabilistic reasoning which, as Thagard shows, can be accommodated within coherence networks only with considerable difficulty. Despite the complexity of the issues it treats, this is not at all a difficult book to read. Thagard has the knack for explaining difficult ideas in readily understandable language. However, his frequent crossing of disciplinary boundaries makes it a challenging task at times to evaluate his theory. Is it primarily a theory of human cognition, of philosophical epistemology, or is it mainly intended as a new procedure for addressing philosophical questions? It is perhaps all of these at once. This kind of blending of psychological and philosophical issues is a familiar feature of the work of contemporary philosophers writing about cognitive science. It is also somewhat reminiscent of the style of thought of many of the great philosophers of the past, who presented their epistemologies as part and parcel of their theories of mind. …

MICSL: Multiple Iterative Constraint Satisfaction based Learning

A novel concept learning algorithm named, MICSL: Multiple Iterative Constraint Satisfaction based Learning, is presented. The algorithm utilizes mathematical programming and constraint satisfaction techniques towards uniform representation and management of both data and background knowledge. It offers a flexible enough learning framework and respective services. The representation flexibility of MICSL rests on a method that transforms propositional cases, represented as propositional clauses, into constraint equivalents. The theoretical background as well as the validity of the transformation process are analyzed and studied. Following a ‘general-to-specific’ generalization strategy the algorithm iterates on multiple calls of a constraint satisfaction process. The outcome is a consistent set of rules. Each rule composes a minimal model of the given set of cases. Theoretical results relating the solutions of a constraint satisfaction process and the minimal models of a set of cases are stated and proved. The performance of the algorithm on some real-world benchmark domains is assessed and compared with widely used machine learning systems, such as C4.5 and CN2. Issues related to the algorithm’s complexity are also raised and discussed.