Theories of contact specified by connection matrices

We begin by characterizing notions of geometric continuity represented by connection matrices. Next we present a set of geometric properties that must be satisfied by all reasonable notions of geometric continuity. These geometric requirements are then reinterpreted as an equivalent collection of algebraic constraints on corresponding sets of connection matrices. We provide a general technique for constructing sets of connection matrices satisfying these criteria and apply this technique to generate many examples of novel notions of geometric continuity. Using these constraints and construction techniques, we show that there is no notion of geometric continuity between reparametrization continuity of order 3, (G³), and Frenet frame continuity of order 3, (F³); that there are several notions of geometric continuity between G⁴ and F⁴; and that the number of different notions of geometric continuity between Gⁿ and Fⁿ grows at least exponentially with n.     

Logic programs with abstract constraint atoms: The role of computations

We provide a new perspective on the semantics of logic programs with arbitrary abstract constraints. To this end, we introduce several notions of computation. We use the results of computations to specify answer sets of programs with constraints. We present the rationale behind the classes of computations we consider, and discuss the relationships among them. We also discuss the relationships among the corresponding concepts of answer sets. One of those concepts has several compelling characterizations and properties, and we propose it as the correct generalization of the answer-set semantics to the case of programs with arbitrary constraints. We show that several other notions of an answer set proposed in the literature for programs with constraints can be obtained within our framework as the results of appropriately selected classes of computations.     

Probabilistic Default Reasoning with Conditional Constraints

We present an approach to reasoning from statistical and subjective knowledge, which is based on a combination of probabilistic reasoning from conditional constraints with approaches to default reasoning from conditional knowledge bases. More precisely, we introduce the notions of z-, lexicographic, and conditional entailment for conditional constraints, which are probabilistic generalizations of Pearl's entailment in system Z, Lehmann's lexicographic entailment, and Geffner's conditional entailment, respectively. We show that the new formalisms have nice properties. In particular, they show a similar behavior as reference-class reasoning in a number of uncontroversial examples. The new formalisms, however, also avoid many drawbacks of reference-class reasoning. More precisely, they can handle complex scenarios and even purely probabilistic subjective knowledge as input. Moreover, conclusions are drawn in a global way from all the available knowledge as a whole. We then show that the new formalisms also have nice general nonmonotonic properties. In detail, the new notions of z-, lexicographic, and conditional entailment have similar properties as their classical counterparts. In particular, they all satisfy the rationality postulates proposed by Kraus, Lehmann, and Magidor, and they have some general irrelevance and direct inference properties. Moreover, the new notions of z- and lexicographic entailment satisfy the property of rational monotonicity. Furthermore, the new notions of z-, lexicographic, and conditional entailment are proper generalizations of both their classical counterparts and the classical notion of logical entailment for conditional constraints. Finally, we provide algorithms for reasoning under the new formalisms, and we analyze its computational complexity.     

Dynamic postural control method for biped in unknown environment

We propose a new way to solve the dynamic equilibrium problem of a biped robot under unknown external perturbations. For that, a multichain mechanical model of a human body (called BIPMAN) is proposed with a general architecture. The principle of the dynamic postural control is based on the correction of the trunk center of mass acceleration and on the force distribution exerted by the limbs on the trunk. To ensure a real time dynamic compensation faced to an unknown external perturbation, we have introduced a new approach called real time criteria and constraints adaptation. This is based on a linear programming technique with a general criterion which optimizes the force distribution. The ponderation coefficients and the task constraints notions are introduced in order to specify criteria and constraints adapted to specific task classes in presence of an unknown external perturbation. Many results are presented to demonstrate criteria and constraints influences on the dynamic equilibrium     

Efficient analysis of pattern-based constraint specifications

Precision and consistency are important prerequisites for class models to conform to their intended domain semantics. Precision can be achieved by augmenting models with design constraints and consistency can be achieved by avoiding contradictory constraints. However, there are different views of what constitutes a contradiction for design constraints. Moreover, state-of-the-art analysis approaches for proving constrained models consistent either scale poorly or require the use of interactive theorem proving. In this paper, we present a heuristic approach for efficiently analyzing constraint specifications built from constraint patterns. This analysis is based on precise notions of consistency for constrained class models and exploits the semantic properties of constraint patterns, thereby enabling syntax-based consistency checking in polynomial-time. We introduce a consistency checker implementing these ideas and we report on case studies in applying our approach to analyze industrial-scale models. These studies show that pattern-based constraint development supports the creation of concise specifications and provides immediate feedback on model consistency.     

Semantic optimization techniques for preference queries

Preference queries are relational algebra or SQL queries that contain occurrences of the winnow operator (find the most preferred tuples in a given relation). Such queries are parameterized by specific preference relations. Semantic optimization techniques make use of integrity constraints holding in the database. In the context of semantic optimization of preference queries, we identify two fundamental properties: containment of preference relations relative to integrity constraints and satisfaction of order axioms relative to integrity constraints. We show numerous applications of those notions to preference query evaluation and optimization. As integrity constraints, we consider constraint-generating dependencies, a class generalizing functional dependencies. We demonstrate that the problems of containment and satisfaction of order axioms can be captured as specific instances of constraint-generating dependency entailment. This makes it possible to formulate necessary and sufficient conditions for the applicability of our techniques as constraint validity problems. We characterize the computational complexity of such problems.     

Compiling Ramification Constraints into Effect Axioms

We present a method to derive a solution to the combined frame and ramification problems for certain classes of theories of action written in the situation calculus. The theories of action considered include the causal laws of the domain, in the form of a set of effect axioms, as well as a set of ramification state constraints. The causal laws state the direct effects that actions have on the world, and ramification state constraints allow one to derive indirect effects of actions on the domain.
To solve the combined frame and ramification problems, the causal laws and ramification state constraints are replaced by a set of successor state axioms . Given a state of the world, these axioms uniquely determine the truth value of dynamic properties after an action is performed. In this article, we extend previous work by formulating an approach for the mechanical generation of these successor state axioms. We make use of the notions of implicate and support that have been developed in the context of propositional theories. The approach works for classes of syntactically restricted sets of ramification state constraints.
     

Comparing the notions of optimality in CP-nets,strategic games and soft constraints

The notion of optimality naturally arises in many areas of applied mathematics and computer science concerned with decision making. Here we consider this notion in the context of three formalisms used for different purposes in reasoning about multi-agent systems: strategic games, CP-nets, and soft constraints. To relate the notions of optimality in these formalisms we introduce a natural qualitative modification of the notion of a strategic game. We show then that the optimal outcomes of a CP-net are exactly the Nash equilibria of such games. This allows us to use the techniques of game theory to search for optimal outcomes of CP-nets and vice-versa, to use techniques developed for CP-nets to search for Nash equilibria of the considered games. Then, we relate the notion of optimality used in the area of soft constraints to that used in a generalization of strategic games, called graphical games. In particular we prove that for a natural class of soft constraints that includes weighted constraints every optimal solution is both a Nash equilibrium and Pareto efficient joint strategy. For a natural mapping in the other direction we show that Pareto efficient joint strategies coincide with the optimal solutions of soft constraints.      

Social choice rules driven by propositional logic

Several rules for social choice are examined from a unifying point of view that looks at them as procedures for revising a system of degrees of belief in accordance with certain specified logical constraints. Belief is here a social attribute, its degrees being measured by the fraction of people who share a given opinion. Different known rules and some new ones are obtained depending on which particular constraints are assumed. These constraints allow to model different notions of choiceness. In particular, we give a new method to deal with approval-disapproval-preferential voting.     

Structural Segmentation of Musical Audio by Constrained Clustering

We describe a method of segmenting musical audio into structural sections based on a hierarchical labeling of spectral features. Frames of audio are first labeled as belonging to one of a number of discrete states using a hidden Markov model trained on the features. Histograms of neighboring frames are then clustered into segment-types representing distinct distributions of states, using a clustering algorithm in which temporal continuity is expressed as a set of constraints modeled by a hidden Markov random field. We give experimental results which show that in many cases the resulting segmentations correspond well to conventional notions of musical form. We show further how the constrained clustering approach can easily be extended to include prior musical knowledge, input from other machine approaches, or semi-supervision.     

Constraint rule-based programming of norms for electronic institutions

Norms constitute a powerful coordination mechanism among heterogeneous agents. In this paper, we propose a rule language to specify and explicitly manage the normative positions of agents (permissions, prohibitions and obligations), with which distinct deontic notions and their relationships can be captured. Our rule-based formalism includes constraints for more expressiveness and precision and allows to supplement (and implement) electronic institutions with norms. We also show how some normative aspects are given computational interpretation.     

Of Coffee Shops and Parking Lots: Considering Matters of Space and Place in the Use of Public Wi-Fi

Wireless local area networks – or Wi-Fi networks – are proliferating in some societies. Our interest in this exploratory essay is to illustrate how ostensibly free, publicly-accessible Wi-Fi requires users to apply conventional understandings of space and place (particularly commercial spaces and places) as they make sense of some ambiguities about proper use in those places. We show, through an examination of the metaphorical terms used to describe Wi-Fi, how spatial notions are employed in an attempt to define ownership of the signal and rights to its use. We consider how place-behaviors require evaluation of legitimacy of users in public places and of hospitality of Wi-Fi providers. We observe that commercial interests underpin notions of ownership, legitimacy and hospitality of social actors in public places like coffee shops and parking lots. As researchers considering matters of participation in virtual places, we must first have some appreciation for the normative constraints and conventions that govern the commercial public places in which users access “free” Wi-Fi.     

Detectability and Stabilizability of Discrete-Time Switched Linear Systems

For discrete-time switched linear systems under nondeterministic autonomous switching, the existence of causal finite-path-dependent stabilizing output injection and state feedback laws are characterized by increasing unions of linear matrix inequality conditions. These convex characterizations lead to the notions of causal finite-path-dependent detectability and stabilizability, which in turn yield a separation result for dynamic output feedback stabilization. By generalizing the standard duality concept to switched systems under arbitrary switching path constraints, we relate these notions to direct extensions of time-varying detectability and stabilizability requirements.      

A type-centric framework for specifying heterogeneous, large-scale, component-oriented, architectures

Maintaining integrity and consistency, and effecting conformance in architectures of large-scale systems require specification and enforcement of many different forms of structural constraints. While type systems have proved effective for enforcing structural constraints in programs and data structures, most architectural modeling frameworks include only weak notions of typing or rely on first order logic constraint languages that have steep learning curves associated with them and that become unwieldy when scaling to large systems.We present the Cadena Architecture Language with Meta-modeling (CALM) — that uses multi-level type systems to specify and enforce a variety of architectural constraints relevant to the development of large-scale component-based systems. Cadena is a robust and extensible tool that has been used to specify a number of industrial strength component models and applied in multiple industrial research projects on model-driven development and software product lines.     

Simulations and bisimulations for analysis of stability with respect to inputs of hybrid systems

Simulation and bisimulation relations define pre-orders on processes which serve as the basis for approximation based verification techniques, and have been extended towards the design of continuous and hybrid systems with complex logic specifications. We study pre-orders between hybrid systems which preserve stability properties with respect to input. We show that these properties are not bisimulation invariant, and hence propose stronger notions which strengthen simulation and bisimulation relations with uniform continuity constraints. We show that uniform continuity is necessary on the relations corresponding to both the state-space and the input-space, and continuity itself does not suffice. Finally, we demonstrate the satisfiability of our definitions by casting the well-known Lyapunov function based techniques for stability analysis as constructing a simple one-dimensional system which is stable and uniformly continuously simulates the original system.     

Solving logic program conflict through strong and weak forgettings

We consider how to forget a set of atoms in a logic program. Intuitively, when a set of atoms is forgotten from a logic program, all atoms in the set should be eliminated from this program in some way, and other atoms related to them in the program might also be affected. We define notions of strong and weak forgettings in logic programs to capture such intuition, reveal their close connections to the notion of forgetting in classical propositional theories, and provide a precise semantic characterization for them. Based on these notions, we then develop a general framework for conflict solving in logic programs. We investigate various semantic properties and features in relation to strong and weak forgettings and conflict solving in the proposed framework. We argue that many important conflict solving problems can be represented within this framework. In particular, we show that all major logic program update approaches can be transformed into our framework, under which each approach becomes a specific conflict solving case with certain constraints. We also study essential computational properties of strong and weak forgettings and conflict solving in the framework.     

On the Stability of Generalized Second Price Auctions with Budgets

The Generalized Second Price (GSP) auction used typically to model sponsored search auctions does not include the notion of budget constraints, which is present in practice. Motivated by this, we introduce the different variants of GSP auctions that take budgets into account in natural ways. We examine their stability by focusing on the existence of Nash equilibria and envy-free assignments. We highlight the differences between these mechanisms and find that only some of them exhibit both notions of stability. This shows the importance of carefully picking the right mechanism to ensure stable outcomes in the presence of budgets.     

A rational reconstruction of the domain of feature structures

Feature structures are employed in various forms in many areas of linguistics. Informally, one can picture a feature structure as a sort of tree decorated with information about constraints requiring that specific subtrees be identical (isomorphic). Here I show that this informal picture of feature structures can be used to characterize exactly the class of feature structures under their usual subsumption ordering. Furthermore, once a precise definition of tree is fixed, this characterization makes use only of standard domain-theoretic notions regarding the information borne by elements in a domain, thus removing (or better, explaining) all apparentlyad hoc choices in the original definition of feature structures. In addition, I show how this characterization can be parameterized in order to yield similar characterizations of various different notions of feature structure, including acyclic structures, structures with appropriateness conditions and structures with apartness conditions (used to model path inequations). The generalizations to other notions of feature structure also emphasize that the construction given here is in fact independent of the application to feature structures.This research has been supported by the Deutsche Forschungsgemeinschaft as part of Sonderforschungsbereich 314, Projekt N3.     

Increasing Constraint Propagation by Redundant Modeling: an Experience Report

This paper describes our experience with a simple modeling and programming approach for increasing the amount of constraint propagation in the constraint solving process. The idea, although similar to redundant constraints, is based on the concept of redundant modeling. We introduce the notions of CSP model and model redundancy, and show how mutually redundant models can be combined and connected using channeling constraints. The combined model contains the mutually redundant models as sub-models. Channeling constraints allow the sub-models to cooperate during constraint solving by propagating constraints freely amongst the sub-models. This extra level of pruning and propagation activities becomes the source of execution speedup. real-life nurse rostering system. We perform two case studies to evaluate the effectiveness and efficiency of our method. The first case study is based on the simple and well-known n-queens problem, while the second case study applies our method in the design and construction of a real-life nurse rostering system. Experimental results provide empirical evidence in line with our prediction.