Intelligent search strategies based on adaptive Constraint Handling Rules

The most advanced implementation of adaptive constraint processing with Constraint Handling Rules (CHR) allows the application of intelligent search strategies to solve Constraint Satisfaction Problems (CSP). This presentation compares an improved version of conflict-directed backjumping and two variants of dynamic backtracking with respect to chronological backtracking on some of the AIM instances which are a benchmark set of random 3-SAT problems. A CHR implementation of a Boolean constraint solver combined with these different search strategies in Java is thus being compared with a CHR implementation of the same Boolean constraint solver combined with chronological backtracking in SICStus Prolog. This comparison shows that the addition of ``intelligence'' to the search process may reduce the number of search steps dramatically. Furthermore, the runtime of their Java implementations is in most cases faster than the implementations of chronological backtracking. More specifically, conflict-directed backjumping is even faster than the SICStus Prolog implementation of chronological backtracking, although our Java implementation of CHR lacks the optimisations made in the SICStus Prolog system. To appear in Theory and Practice of Logic Programming (TPLP).Comment: Number of pages: 27 Number of figures: 14 Number of Tables:

Constraint-based reachability

Iterative imperative programs can be considered as infinite-state systems computing over possibly unbounded domains. Studying reachability in these systems is challenging as it requires to deal with an infinite number of states with standard backward or forward exploration strategies. An approach that we call Constraint-based reachability, is proposed to address reachability problems by exploring program states using a constraint model of the whole program. The keypoint of the approach is to interpret imperative constructions such as conditionals, loops, array and memory manipulations with the fundamental notion of constraint over a computational domain. By combining constraint filtering and abstraction techniques, Constraint-based reachability is able to solve reachability problems which are usually outside the scope of backward or forward exploration strategies. This paper proposes an interpretation of classical filtering consistencies used in Constraint Programming as abstract domain computations, and shows how this approach can be used to produce a constraint solver that efficiently generates solutions for reachability problems that are unsolvable by other approaches.Comment: In Proceedings Infinity 2012, arXiv:1302.310

Linear Transmission of Composite Gaussian Measurements over a Fading Channel under Delay Constraints

Delay constrained linear transmission (LT) strategies are considered for the transmission of composite Gaussian measurements over an additive white Gaussian noise fading channel under an average power constraint. If the channel state information (CSI) is known by both the encoder and decoder, the optimal LT scheme in terms of the average mean-square error distortion is characterized under a strict delay constraint, and a graphical interpretation of the optimal power allocation strategy is presented. Then, for general delay constraints, two LT strategies are proposed based on the solution to a particular multiple measurements-parallel channels scenario. It is shown that the distortion decreases as the delay constraint is relaxed, and when the delay constraint is completely removed, both strategies achieve the optimal performance under certain matching conditions. If the CSI is known only by the decoder, the optimal LT strategy is derived under a strict delay constraint. The extension to general delay constraints is elusive. As a first step towards understanding the structure of the optimal scheme in this case, it is shown that for the multiple measurementsparallel channels scenario, any LT scheme that uses only a oneto-one linear mapping between measurements and channels is suboptimal in general

Delay-Optimal Buffer-Aware Probabilistic Scheduling with Adaptive Transmission

Cross-layer scheduling is a promising way to improve Quality of Service (QoS) given a power constraint. In this paper, we investigate the system with random data arrival and adaptive transmission. Probabilistic scheduling strategies aware of the buffer state are applied to generalize conventional deterministic scheduling. Based on this, the average delay and power consumption are analysed by Markov reward process. The optimal delay-power tradeoff curve is the Pareto frontier of the feasible delay-power region. It is proved that the optimal delay-power tradeoff is piecewise-linear, whose vertices are obtained by deterministic strategies. Moreover, the corresponding strategies of the optimal tradeoff curve are threshold-based, hence can be obtained by a proposed effective algorithm. On the other hand, we formulate a linear programming to minimize the average delay given a fixed power constraint. By varying the power constraint, the optimal delay-power tradeoff curve can also be obtained. It is demonstrated that the algorithm result and the optimization result match each other, and are further validated by Monte-Carlo simulation.Comment: 6 pages, 4 figures, accepted by IEEE ICCC 201

Constraint-Based Supply Chain Inventory Deployment Strategies

The development of Supply Chain Management has occurred gradually over the latter half of the last century, and in this century will continue to evolve in response to the continual changes in the business environment. As organizations exhaust opportunities for internal breakthrough improvements, they will increasingly turn toward the supply chain for an additional source of untapped improvements. Manufacturers in particular can benefit from this increased focus on the chain, but the gains realized will vary by the type of supply chain. By applying basic production control principles to the chain, and effectively using tools already common at the production line level, organizations address important supply chain considerations. Both the Theory of Constraints and the factory physics principles behind the Constant WIP concepts focus on the system constraint with the aim of controlling inventory. Each can be extrapolated to focus on a system whose boundaries span the entire supply chain

Generalization Strategies for the Verification of Infinite State Systems

We present a method for the automated verification of temporal properties of infinite state systems. Our verification method is based on the specialization of constraint logic programs (CLP) and works in two phases: (1) in the first phase, a CLP specification of an infinite state system is specialized with respect to the initial state of the system and the temporal property to be verified, and (2) in the second phase, the specialized program is evaluated by using a bottom-up strategy. The effectiveness of the method strongly depends on the generalization strategy which is applied during the program specialization phase. We consider several generalization strategies obtained by combining techniques already known in the field of program analysis and program transformation, and we also introduce some new strategies. Then, through many verification experiments, we evaluate the effectiveness of the generalization strategies we have considered. Finally, we compare the implementation of our specialization-based verification method to other constraint-based model checking tools. The experimental results show that our method is competitive with the methods used by those other tools. To appear in Theory and Practice of Logic Programming (TPLP).Comment: 24 pages, 2 figures, 5 table

Exploring efficacy in personal constraint negotiation: an ethnography of mountaineering tourists

Limited work has explored the relationship between efficacy and personal constraint negotiation for adventure tourists, yet efficacy is pivotal to successful activity participation as it influences people’s perceived ability to cope with constraints, and their decision to use negotiation strategies. This paper explores these themes with participants of a commercially organised mountaineering expedition. Phenomenology-based ethnography was adopted to appreciate the social and cultural mountaineering setting from an emic perspective. Ethnography is already being used to understand adventure participation, yet there is considerable scope to employ it further through researchers immersing themselves into the experience. The findings capture the interaction between the ethnographer and the group members, and provide an embodied account using their lived experiences. Findings reveal that personal mountaineering skills, personal fitness, altitude sickness and fatigue were the four key types of personal constraint. Self-efficacy, negotiation-efficacy and other factors, such as hardiness and motivation, influenced the effectiveness of negotiation strategies. Training, rest days, personal health, and positive self-talk were negotiation strategies. A conceptual model illustrates these results and demonstrates the interplay between efficacy and the personal constraint negotiation journey for led mountaineers

Benchmarking Signorini and exponential contact laws for an industrial train brake squeal application

Contact representation of structure interactions for finite element models is nowadays of great interest in the industry. Two contact modellig strategies exist in the literature, either based on a perfect contact with no interpenetration of structures at contact points, or based on functional laws releasing the contact constraint through pressure-penetration relationships. Both strategies require very different and rarely documented numerical implementations, making difficult any objective comparison. This paper presents a benchmark between ideal contact and a functional law of the exponential type applied to squeal simulations by complex mode analysis of an industrial railway brake