A linear proportional effort allocation rule

This paper proposes a new class of allocation rules in network games. Like the solution theory in cooperative games of how the Harsanyi dividend of each coalition is distributed among a set of players, this new class of allocation rules focuses on the distribution of the dividend of each network. The dividend of each network is allocated in proportion to some measure of each player’s effort, which is called an effort function. With linearity of the allocation rules, an allocation rule is specified by the effort functions. These types of allocation rules are called linear proportional effort allocation rules. Two famous allocation rules, the Myerson value and the position value, belong to this class of allocation rules. In this study, we provide a unifying approach to define the two aforementioned values. Moreover, we provide an axiomatic analysis of this class of allocation rules, and axiomatize the Myerson value, the position value, and their non-symmetric generalizations in terms of effort functions. We propose a new allocation rule in network games that also belongs to this class of allocation rules.     

An estimation of distribution algorithm for nurse scheduling

Schedules can be built in a similar way to a human scheduler by using a set of rules that involve domain knowledge. This paper presents an Estimation of Distribution Algorithm (EDA) for the nurse scheduling problem, which involves choosing a suitable scheduling rule from a set for the assignment of each nurse. Unlike previous work that used Genetic Algorithms (GAs) to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The EDA is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems.     

A noncooperative approach to bankruptcy problems with an endogenous estate

We introduce a new class of bankruptcy problems in which the value of the estate is endogenous and depends on agents’ investment decisions. There are two investment alternatives: investing in a company (risky asset) and depositing money into a savings account (risk-free asset). Bankruptcy is possible only for the risky asset. We define a game between agents each of which aims to maximize his expected payoff by choosing an investment alternative and a company management which aims to maximize profits by choosing a bankruptcy rule. Our agents are differentiated by their incomes. We consider three most prominent bankruptcy rules in our base model: the proportional rule, the constrained equal awards rule and the constrained equal losses rule. We show that only the proportional rule is a part of any pure strategy subgame perfect Nash equilibrium. This result is robust to changes in income distribution in the economy and can be extended to a larger set of bankruptcy rules and multiple types. However, extension to multiple company framework with competition leads to equilibria where the noncooperative support for the proportional rule disappears.     

Allocation rules for coalitional network games

Coalitional network games are real-valued functions defined on a set of players organized into a network and a coalition structure. We adopt a flexible approach assuming that players organize themselves the best way possible by forming the efficient coalitional network structure. We propose two allocation rules that distribute the value of the efficient coalitional network structure: the atom-based flexible coalitional network allocation rule and the player-based flexible coalitional network allocation rule.     

A Dual-Objective Evolutionary Algorithm for Rules Extraction in Data Mining

This paper presents a dual-objective evolutionary algorithm (DOEA) for extracting multiple decision rule lists in data mining, which aims at satisfying the classification criteria of high accuracy and ease of user comprehension. Unlike existing approaches, the algorithm incorporates the concept of Pareto dominance to evolve a set of non-dominated decision rule lists each having different classification accuracy and number of rules over a specified range. The classification results of DOEA are analyzed and compared with existing rule-based and non-rule based classifiers based upon 8 test problems obtained from UCI Machine Learning Repository. It is shown that the DOEA produces comprehensible rules with competitive classification accuracy as compared to many methods in literature. Results obtained from box plots and t-tests further examine its invariance to random partition of datasets. An erratum to this article is available at .     

Emergent behavior in two complex cellular automata rule sets

Cellular automata systems often produce complex behavior from simple rule sets. The behaviors and results of two complex combinations of cellular automata rules are analyzed. Both two‐dimensional rule sets add complexities to typical cellular automata systems by attaching attributes and rules to each cell. One of the rule sets produces gliders that reproduce upon collision, whereas the other grows into an intricate shape. Projection and entropy analysis classify the rule sets as complex for the intricate shape, but measurements indicate that the self‐reproducing gliders fall between ordered and complex classification, despite their complex appearance. © 2005 Wiley Periodicals, Inc. Complexity 10: 45–55, 2005     

A thermodynamical framework for chemically reacting systems

In this paper, we develop a thermodynamic framework that is capable of describing the response of viscoelastic materials that are undergoing chemical reactions that takes into account stoichiometry. Of course, as a special sub-case, we can also describe the response of elastic materials that undergo chemical reactions. The study generalizes the framework developed by Rajagopal and co-workers to study the response of a disparate class of bodies undergoing entropy producing processes. One of the quintessential feature of this framework is that the second law of thermodynamics is formulated by introducing Gibbs?? potential, which is the natural way to study problems involving chemical reactions. The Gibbs potential?Cbased formulation also naturally leads to implicit constitutive equations for the stress tensor. Another feature of the framework is that the constraints due to stoichiometry can also be taken into account in a consistent manner. The assumption of maximization of the rate of entropy production due to dissipation, heat conduction, and chemical reactions is invoked to determine an equation for the evolution of the natural configuration ?? _p(t)(B), the heat flux vector and a novel set of equations for the evolution of the concentration of the chemical constituents. To determine the efficacy of the framework with regard to chemical reactions, those occurring during vulcanization, a challenging set of chemical reactions, are chosen. More than one type of reaction mechanism is considered and the theoretically predicted distribution of mono, di and polysulfidic cross-links agree reasonably well with available experimental data.     

Evidence based rules and learning in symmetric normal-form games

We put forth a general theory of boundedly rational behavior and learning for symmetric normal-form games with unique symmetric Nash equilibria. A class of evidence-based behavioral rules is specified, which includes best-responding to a prior and Nash play. A player begins with initial propsenities towards the rules, and given experience over time adjusts his/her propensities in proportion to the past performance of the rules. We focus on scenarios in which the past distribution of play is revealed to all players. Confronting this theory with experimental data, we find significant support for rule learning and heterogeneity among participants. Received June 1996/Final version April 1997     

On the selection of subdivision directions in interval branch-and-bound methods for global optimization

This paper investigates the influence of the interval subdivision selection rule on the convergence of interval branch-and-bound algorithms for global optimization. For the class of rules that allows convergence, we study the effects of the rules on a model algorithm with special list ordering. Four different rules are investigated in theory and in practice. A wide spectrum of test problems is used for numerical tests indicating that there are substantial differences between the rules with respect to the required CPU time, the number of function and derivative evaluations, and the necessary storage space. Two rules can provide considerable improvements in efficiency for our model algorithm.The work has been supported by the Grants OTKA 2879/1991, and MKM 414/1994.     

A Genetic Approach for Solving a Scheduling Problem in a Robotized Analytical System

We consider a robotized analytical system in which a chemical treatment has to be performed on a given set of identical samples. The objective is to carry out the chemical treatment on the whole set of samples in the shortest possible time. All constraints have to be satisfied since a modification of the chemical process could create unexpected reactions.We have developed a new robust method governed by a genetic algorithm to solve this scheduling problem. The crossover mechanism of this evolutionary method is based on an extension of the uniform crossover introduced by Syswerda (1989).The proposed approach can be adapted to other combinatorial problems where decisions, based on rules, have to be taken at each step of a constructive method.     

New fuzzy wavelet network for modeling and control: The modeling approach

In this paper, a fuzzy wavelet network is proposed to approximate arbitrary nonlinear functions based on the theory of multiresolution analysis (MRA) of wavelet transform and fuzzy concepts. The presented network combines TSK fuzzy models with wavelet transform and ROLS learning algorithm while still preserve the property of linearity in parameters. In order to reduce the number of fuzzy rules, fuzzy clustering is invoked. In the clustering algorithm, those wavelets that are closer to each other in the sense of the Euclidean norm are placed in a group and are used in the consequent part of a fuzzy rule. Antecedent parts of the rules are Gaussian membership functions. Determination of the deviation parameter is performed with the help of gold partition method. Here, mean of each function is derived by averaging center of all wavelets that are related to that particular rule. The overall developed fuzzy wavelet network is called fuzzy wave-net and simulation results show superior performance over previous networks.The present work is complemented by a second part which focuses on the control aspects and to be published in this journal([17]). This paper proposes an observer based self-structuring robust adaptive fuzzy wave-net (FWN) controller for a class of nonlinear uncertain multi-input multi-output systems.     

Evolution of fuzzy classifiers using genetic programming

In this paper, we propose a genetic programming (GP) based approach to evolve fuzzy rule based classifiers. For a c-class problem, a classifier consists of c trees. Each tree, T _i , of the multi-tree classifier represents a set of rules for class i. During the evolutionary process, the inaccurate/inactive rules of the initial set of rules are removed by a cleaning scheme. This allows good rules to sustain and that eventually determines the number of rules. In the beginning, our GP scheme uses a randomly selected subset of features and then evolves the features to be used in each rule. The initial rules are constructed using prototypes, which are generated randomly as well as by the fuzzy k-means (FKM) algorithm. Besides, experiments are conducted in three different ways: Using only randomly generated rules, using a mixture of randomly generated rules and FKM prototype based rules, and with exclusively FKM prototype based rules. The performance of the classifiers is comparable irrespective of the type of initial rules. This emphasizes the novelty of the proposed evolutionary scheme. In this context, we propose a new mutation operation to alter the rule parameters. The GP scheme optimizes the structure of rules as well as the parameters involved. The method is validated on six benchmark data sets and the performance of the proposed scheme is found to be satisfactory.     

Impact of binary and Fibonacci expansions of numbers on winning strategies for Nim-like games

An analysis of the successful moves for the game of solitaire, played in reverse, has resulted in the formulation of a set of rules, the observance of which leads to success. The principal rules entail symmetry properties and also the density of packing of pegs. Subsidiary rules are formulated in terms of both the current location of play and its direction. When play has proceeded for some way, a difficulty is encountered which can be surmounted by applying a modified form of the rule for density of packing.

The interesting fact emerges that within the game of solitaire there are two separate symmetrical situations which are species of mini‐solitaire.     

Non-Periodic Rhomb Substitution Tilings that Admit Order n Rotational Symmetry

For each n ∈ ℕ we construct a substitution rule using the set of rhombs with angles (πk)/n. These substitution rules generate a local isomorphism class of tilings closed under rotation of order 2n, and also admit singular tilings fixed under a rotation of order n. The scaling factors for this set of substitution rules includes algebraic numbers of every rank.     

The Orienteering Problem with Time Windows

The orienteering problem with time windows, denoted by OPTW, belongs to a class of routeing and scheduling problems that arise in physical distribution. It may be modelled as a problem on a graph. It considers a set of nodes (customers), each with an associated profit and service duration (time window), and a set of arcs, each with an associated travel time. The objective of the problem is to construct an acyclic path beginning at a specified origin and ending at a specified destination that maximizes the total profit while observing time window constraints on all nodes and not exceeding a designated time limit. The problem is classified as NP-hard and, thus, an exact algorithm that executes in reasonable computational time is unlikely to exist. Since the problem is highly-constrained, we were able to develop a heuristic (referred to as the ‘tree’ heuristic) based upon an exhaustive search of the feasible solution space. The tree heuristic systematically generates a list of feasible paths and then selects the most profitable path from the list. In comparison with an insertion heuristic, the tree heuristic was found to produce improved values of total profit for heavily-constrained, modest-sized problems with reasonable computational effort.     

Bi-criteria optimization problems for decision rules

We consider bi-criteria optimization problems for decision rules and rule systems relative to length and coverage. We study decision tables with many-valued decisions in which each row is associated with a set of decisions as well as single-valued decisions where each row has a single decision. Short rules are more understandable; rules covering more rows are more general. Both of these problems—minimization of length and maximization of coverage of rules are NP-hard. We create dynamic programming algorithms which can find the minimum length and the maximum coverage of rules, and can construct the set of Pareto optimal points for the corresponding bi-criteria optimization problem. This approach is applicable for medium-sized decision tables. However, the considered approach allows us to evaluate the quality of various heuristics for decision rule construction which are applicable for relatively big datasets. We can evaluate these heuristics from the point of view of (i) single-criterion—we can compare the length or coverage of rules constructed by heuristics; and (ii) bi-criteria—we can measure the distance of a point (length, coverage) corresponding to a heuristic from the set of Pareto optimal points. The presented results show that the best heuristics from the point of view of bi-criteria optimization are not always the best ones from the point of view of single-criterion optimization.     

Semantics for possibilistic answer set programs: Uncertain rules versus rules with uncertain conclusions

Although Answer Set Programming (ASP) is a powerful framework for declarative problem solving, it cannot in an intuitive way handle situations in which some rules are uncertain, or in which it is more important to satisfy some constraints than others. Possibilistic ASP (PASP) is a natural extension of ASP in which certainty weights are associated with each rule. In this paper we contrast two different views on interpreting the weights attached to rules. Under the first view, weights reflect the certainty with which we can conclude the head of a rule when its body is satisfied. Under the second view, weights reflect the certainty that a given rule restricts the considered epistemic states of an agent in a valid way, i.e. it is the certainty that the rule itself is correct. The first view gives rise to a set of weighted answer sets, whereas the second view gives rise to a weighted set of classical answer sets.     

The ultratriangular form for prime-power lattice rules

A recent development in the theory of lattice rules has been the introduction of the unique ultratriangular D-Z form for prime-power rules. It is known that any lattice rule may be decomposed into its Sylow p-components. These components are prime-power rules, each of which has a unique ultratriangular form. By reassembling these ultratriangular forms in a defined way, it is possible to obtain a canonical form for any lattice rule. A special case occurs when the ultratriangular forms of each of the Sylow p-components have a consistent set of column indices. In this case, it is possible to obtain a unique canonical D-Z form. Given the column indices and the invariants for an ultratriangular form, we may obtain a formula for the number of ultratriangular forms, and hence the number of prime-power lattice rules, having these column indices and invariants.     

Dynamic priority rule-based forward-backward heuristic algorithm for resource levelling problem in construction project

Resource levelling aims at minimizing the fluctuation of resource usage, which is accomplished by shifting non-critical activities within their float according to some heuristic rules. Most of these rules adopted a unidirectional scheduling based on a static priority rule. In this paper, we propose a dynamic priority rule-based forward-backward heuristic algorithm (FBHA). The FBHA optimizes resource allocation by shifting non-critical activities within their forward free float (FFF), forward total float (FTF) and backward free float (BFF), successively. A project is divided into several phases during each forward/backward scheduling module. In each phase, the shifting sequence and days of non-critical activities depend on a dynamic priority rule set. The FBHA is integrated into the Microsoft Project 2007 commercial software package to improve the performance of the software and facilitate the project planners. One example is analysed to illustrate the iteration process of the proposed FBHA. Another example with multiple precedence constraints is used to demonstrate the effectiveness of the proposed FBHA in complicated construction projects.     

A note on admissible rules and the disjunction property in intermediate logics

With any structural inference rule A/B, we associate the rule ${(A \lor p)/(B \lor p)}$ , providing that formulas A and B do not contain the variable p. We call the latter rule a join-extension ( ${\lor}$ -extension, for short) of the former. Obviously, for any intermediate logic with disjunction property, a ${\lor}$ -extension of any admissible rule is also admissible in this logic. We investigate intermediate logics, in which the ${\lor}$ -extension of each admissible rule is admissible. We prove that any structural finitary consequence operator (for intermediate logic) can be defined by a set of ${\lor}$ -extended rules if and only if it can be defined through a set of well-connected Heyting algebras of a corresponding quasivariety. As we exemplify, the latter condition is satisfied for a broad class of algebraizable logics.