Lightweight dynamic symmetry breaking

Symmetries in constraint problems present an opportunity for reducing search. This paper presents Lightweight Dynamic Symmetry Breaking, an automatic symmetry breaking method that is efficient enough to be used as a default, since it never yields a major slowdown while often giving major performance improvements. This is achieved by automatically exploiting certain kinds of symmetry that are common, can be compactly represented, easily and efficiently processed, automatically detected, and lead to large reductions in search. Moreover, the method is easy to implement and integrate in any constraint system. Experimental results show the method is competitive with the best symmetry breaking methods without risking poor performance.     

A recognition method based on collective decision making using systems of regularities of various types

A new recognition method that implies weighted voting on systems of “syndromes,” i.e., subregions of the attribute space where objects of one class dominate, is given. It is a modified version of statistically weighted syndromes developed previously. To find syndromes, it searches for optimal partitions within several models of different levels of complexity. Syndromes to be included in the final set used in collective decision making are selected by the criterion for the partitioning degree of classes and by the parameter related to the complexity of the partitioning model involved. The weighted voting procedure can be interpreted as the convex correction of sets of predictors. The generalizing potential of such procedures is discussed. Experimental results of comparing the given method with the previous version (SWS) and alternative techniques are presented. To estimate the efficiency, several criteria are used, including a way to analyze recognition accuracy on the totality of all possible decision rules (ROC analysis).     

Static and dynamic structural symmetry breaking

We reconsider the idea of structural symmetry breaking for constraint satisfaction problems (CSPs). We show that the dynamic dominance checks used in symmetry breaking by dominance-detection search for CSPs with piecewise variable and value symmetries have a static counterpart: there exists a set of constraints that can be posted at the root node and that breaks all the compositions of these (unconditional) symmetries. The amount of these symmetry-breaking constraints is linear in the size of the problem, and yet they are able to remove a super-exponential number of symmetries on both values and variables. Moreover, we compare the search trees under static and dynamic structural symmetry breaking when using fixed variable and value orderings. These results are then generalised to wreath-symmetric CSPs with both variable and value symmetries. We show that there also exists a polynomial-time dominance-detection algorithm for this class of CSPs, as well as a linear-sized set of constraints that breaks these symmetries statically.     

Naturalistic decision making

OBJECTIVE: This article describes the origins and contributions of the naturalistic decision making (NDM) research approach. BACKGROUND: NDM research emerged in the 1980s to study how people make decisions in real-world settings. Method: The findings and methods used by NDM researchers are presented along with their implications. RESULTS: The NDM framework emphasizes the role of experience in enabling people to rapidly categorize situations to make effective decisions. CONCLUSION: The NDM focus on field settings and its interest in complex conditions provide insights for human factors practitioners about ways to improve performance. APPLICATION: The NDM approach has been used to improve performance through revisions of military doctrine, training that is focused on decision requirements, and the development of information technologies to support decision making and related cognitive functions.     

Local and global symmetry breaking in itemset mining

The concept of symmetry has been extensively studied in the field of constraint programming and in the propositional satisfiability. Several methods for detection and removal of these symmetries have been developed, and their use in known solvers of these domains improved dramatically their effectiveness on a big variety of problems considered difficult to solve. The concept of symmetry may be exported to other areas where some structures can be exploited effectively. Particularly, in the area of data mining where some tasks can be expressed as constraints or logical formulas. We are interested here, by the detection and elimination of local and global symmetries in the item-set mining problem. Recent works have provided effective encodings as Boolean constraints for these data mining tasks and some idea on symmetry elimination in this area begin to appear, but still few and the techniques presented are often on global symmetry that is detected and eliminated statically in a preprocessing phase. In this work we study the notion of local symmetry and compare it to global symmetry for the itemset mining problem. We show how local symmetries of the boolean encoding can be detected dynamically and give some properties that allow to eliminate theses symmetries in SAT-based itemset mining solvers in order to enhance their efficiency.     

Norms in artificial decision making

A method for forcing norms onto individual agents in a multi-agent system is presented. The agents under study are supersoft agents: autonomous artificial agents programmed to represent and evaluate vague and imprecise information. Agents are further assumed to act in accordance with advice obtained from a normative decision module, with which they can communicate. Norms act as global constraints on the evaluations performed in the decision module and hence no action that violates a norm will be suggested to any agent. Further constraints on action may then be added locally. The method strives to characterise real-time decision making in agents, in the presence of risk and uncertainty.     

Graphics in business decision making

Mountains of data can be converted into easily digested graphs so that decision makers can grasp the peaks and trends of economic situations and make rapid and confidential decisions. We will study how we can define simple models using a graphic-based Industrial Dynamics system (Forrester of MIT), then interact with them symbionically to obtain graphs of various manufacturing or sales considerations. We will proceed to apply graphic analysis techniques to these graphs in order to produce curves that identify the sensitivities of the analysis, to reinforce confidence in the decision to be made. Finally, we will discuss the progress and the potential of graphics in business decision making.     

Variable use of standards-based IOS enabling technologies in Australian SMEs: an examination of deliberate and emergent decision making processes

Use of inter-organizational systems (IOS) is widely recognized as pivotal to organizational success. However, the nature of decision making processes regarding the adoption and use of IOS-enabling technologies has received little research attention. The authors explore approaches to decision making relating to SMEs’ use of these technologies and outline the drivers and implications of such decision making. Data were collected through two crosssectional surveys and multiple case studies. The surveys allowed for the identification of two groups of three SMEs each—the first group increased and the second group decreased the use of IOS-enabling technologies over time. This identification provided the context of strategic change or transition in the use of these technologies over time. The study’s results challenge widely held assumptions about the performance benefits of higher-level, deliberate planning over functional-level, emergent decision making regarding SMEs’ technological choices. Results also show that SME managers mainly use flexible IOS technology adoption and implementation strategies to promote organizational performance. Further, two main factors drive flexible decision making: a lack of managerial power and a lack of financial resources. Overall, the study offers insights into the link between IOS-enabling technology use and organizational strategy, and advances research concerning the contingencies influencing SMEs’ decision making in this context.     

Reliability of decision making in human-organizations

In a human-organization system with n officers, a decision has to be made on whether to accept or to reject an innovation-oriented proposal that can either be good or bad. Each officer will review a given set of available information regarding the proposal in question and will thereafter return his or her decision. In the common formulation of this problem, the organization will accept the proposal if at least k officers decide for the project, where k is a prespecified threshold value. Hereby, each officer is subject to different errors and therefore, the final decision may be faulty as well. The reliability of such a system is the probability for the organization to make the right decision. In this paper, by imposing weights for each officer, generalized problem formulations are introduced and formulas for the corresponding reliability are derived     

Partial symmetry breaking by local search in the group

The presence of symmetry in constraint satisfaction problems can cause a great deal of wasted search effort, and several methods for breaking symmetries have been reported. In this paper we describe a new method called Symmetry Breaking by Nonstationary Optimisation, which interleaves local search in the symmetry group with backtrack search on the constraint problem. It can be tuned to break each symmetry with an arbitrarily high probability with high runtime overhead, or as a lightweight but still powerful method with low runtime overhead. It has negligible memory requirement, it combines well with static lex-leader constraints, and its benefit increases with problem hardness.     

Systematic decision process for intelligent decision making

In this paper, Systematic decision process (SDP) for solving Multiple Criteria Decision Making problems with application for manufacturing location selection is introduced. SDP is a comprehensive approach which is based on eliciting strength of preferences for assessing additive utility functions. SDP consists of three steps: I. assessing weights, II. assessing qualitative criteria, and III. ranking alternatives using the assessed additive utility function. Strengths of preferences can be expressed by using either qualitative or numerical ratings. If the decision maker is inconsistent in his/her responses, such inconsistencies are identified by the method. It is shown that the method has advantages in terms of simplicity and accuracy compared to existing methods such as Analytical Hierarchy Process. Furthermore, a quadratic optimization method for assessing weights of additive utility function by use of pair comparison of actual alternatives is developed. Computational experiments are provided.     

Multiknowledge for decision making

The representation of knowledge has an important effect on automated decision-making. In this paper, vector spaces are used to describe a condition space and a decision space, and knowledge is represented by a mapping from the condition space to the decision space. Many such mappings can be obtained from a training set. A set of mappings, which are created from multiple reducts in the training set, is defined as multiknowledge. In order to get a good reduct and find multiple reducts, the WADF (worst-attribute-drop-first) algorithm is developed through analysis of the properties of decision systems using rough set theory. An approach that combines multiknowledge and the naïve Bayes classifier is applied to make decisions for unseen instances or for instances with missing attribute values. Benchmark data sets from the UCI Machine Learning Repository are used to test the algorithms. The experimental results are encouraging; the prediction accuracy for unseen instances by using the algorithms is higher than by using other approaches based on a single body of knowledge.     

Pareto multi-criteria decision making

The paper concerns design governed by multiple objective criteria, which are conflicting in the sense of competing for common resources to achieve variously different performance objectives (financial, functional, environmental, esthetical, etc.). A multi-criteria decision making (MCDM) strategy is proposed that employs a tradeoff-analysis technique to identify compromise designs for which the competing criteria are mutually satisfied in a Pareto-optimal sense. The MCDM strategy is initially developed for the case of design governed by n = 2 objective criteria. It is then extended to design governed by n > 2 objective criteria, by introducing the concept of primary and aggregate criteria. It is proved that, from among the theoretically infinite number of feasible designs forming the Pareto front for a design problem governed by n independent objective criteria, there exists a unique Pareto-compromise design that represents a mutually agreeable tradeoff between all n criteria. This remarkable result is illustrated for a flexural plate design governed by n = 2 criteria, a bridge maintenance-intervention protocol design governed by n = 3 criteria, and a media centre envelop design governed by n = 11 criteria.     

Distributing identity [symmetry breaking distributed accessprotocols]

In the next few years, computational, sensory, and communication capabilities will diffuse out of their present home in beige boxes on desktops and into everyday objects such as furniture, clothing, and other non-technological objects. As the cost of electronics continues to drop, the number of activated, networked devices will grow rapidly. Each device sharing a particular multiaccess channel will need a unique identifier for that channel. Present communication protocols such as Ethernet specify that the manufacturers must coordinate with one another in order to avoid assigning the same ID twice. The article explores methods by which the devices could coordinate with one another to manage ID assignment dynamically and automatically. In particular, the article is an investigation of distributed protocols that utilize physical sources of symmetry breaking to enable a network of initially identical units to acquire the unique identities required for point-to-point communication over a shared resource such as a bus or common RF band. It presents several protocols, compares their resource use (time, random bits, space, communication), and notes a trade-off among these measures of algorithm complexity     

Lifting symmetry breaking constraints with inductive logic programming

Machine Learning - Efficient omission of symmetric solution candidates is essential for combinatorial problem-solving. Most of the existing approaches are instance-specific and focus on the...     

Modelling collective decision making in groups and crowds: Integrating social contagion and interacting emotions, beliefs and intentions

Collective decision making involves on the one hand individual mental states such as beliefs, emotions and intentions, and on the other hand interaction with others with possibly different mental states. Achieving a satisfactory common group decision on which all agree requires that such mental states are adapted to each other by social interaction. Recent developments in social neuroscience have revealed neural mechanisms by which such mutual adaptation can be realised. These mechanisms not only enable intentions to converge to an emerging common decision, but at the same time enable to achieve shared underlying individual beliefs and emotions. This paper presents a computational model for such processes. As an application of the model, an agent-based analysis was made of patterns in crowd behaviour, in particular to simulate a real-life incident that took place on May 4, 2010 in Amsterdam. From available video material and witness reports, useful empirical data were extracted. Similar patterns were achieved in simulations, whereby some of the parameters of the model were tuned to the case addressed, and most parameters were assigned default values. The results show the inclusion of contagion of belief, emotion, and intention states of agents results in better reproduction of the incident than non-inclusion.     

Dynamic structural symmetry breaking for constraint satisfaction problems

In recent years, symmetry breaking for constraint satisfaction problems (CSPs) has attracted considerable attention. Various general schemes have been proposed to eliminate symmetries. In general, these schemes may take exponential space or time to eliminate all the symmetries. We identify several classes of CSPs that encompass many practical problems and for which symmetry breaking for various forms of value or variable interchangeability is tractable using dedicated search procedures. We also show the limits of efficient symmetry breaking for such dominance-detection schemes by proving intractability results for some classes of CSPs.     

Principles of reference-aided decision making

A unified approach to decision making with extremal many-criteria models, expert estimates, and voting is developed on the basis of reference and distance concepts and taxonomy methods. It involves the solution of many-criteria problems with the Pareto optimum. Examples demonstrating the effectiveness and universality of the approach are given.