Representation Of Rules Research Articles

Constructing enhanced default theories incrementally

The main difference between various formalisms of non-monotonic reasoning is the representation of non-monotonic rules. In default logic, they are represented by special expressions called defaults. In default logic, commonsense knowledge about the world is represented as a set of named defaults. The use of defaults is popular because they reduce the complexity of the representation, and they are sufficient for knowledge representation in many naturally occurring contexts. This paper offers an incremental process to acquire defaults from human experts directly and at the same time it provides added semantics to defaults by adding priorities to defaults and creating additional relations between them. The paper uses an existing incremental framework, NRDR, to generate these defaults. This framework is chosen as it not only enables incremental context driven formulation of defaults, but also allows experts to introduce their own domain terms. In choosing this framework, the paper broadens its utility.

Biosystem models, generated from a complex rule/reaction/influence network and from two functionality prototypes

In this work we have further developed the Direct Computer Mapping (DCM) based modelling and simulation methodology. A unified, transition-based representation of complex rule, reaction and influence networks has been introduced and two prototypes (one general state- and another general transition-prototype) have been developed for the unified functional modelling of the state and transition nodes. Starting from the network and from the functional prototypes, an automatic generation method of the graphically editable and extensible GraphML description of biosystem models has been elaborated. The new developments have been implemented in the improved kernel of DCM models. The applied knowledge representation makes possible the unified generation and execution of the balance-based quantitative and influence- or rule-based qualitative, as well as optionally time-driven, multiscale biosystem models. Application of the developed methodology has been illustrated by the improved implementation of the formerly studied and upgraded example biosystem model for combining the detailed, quantitative p53/miR34a signalling system with the pathological model through an extended rule-based coupling model.

Rule Encoding in Orbitofrontal Cortex and Striatum Guides Selection.

Rule maintenance, an important executive function, is generally thought to rely on dorsolateral brain regions. In this study, we examined activity of single neurons in orbitofrontal cortex and in ventral and dorsal striatum of macaques in a Wisconsin Card Sorting Task. Neurons in all three areas encoded rules and rule categories robustly. Rule identity also affected neural responses to potential choice options, suggesting that stored information is used to influence decisions. These results endorse the hypothesis that rule maintenance is a broadly distributed mental operation.

Interval-Valued Belief Rule Inference Methodology Based on Evidential Reasoning-IRIMER

In this paper, an interval-valued belief rule inference methodology based on evidential reasoning (IRIMER) is proposed, which includes the interval-valued belief rule representation scheme and its inference methodology. This interval-valued belief rule base is designed with interval-valued belief degrees embedded in both the consequents and the antecedents of each rule, which can represent uncertain information or knowledge more flexible and reasonable than the previous belief rule base. Then its inference methodology is developed on the interval-valued evidential reasoning (IER) approach. The IRIMER approach improves and extends the recently uncertainty inference methods from the rule representation scheme and the inference framework. Finally, a case is studied to demonstrate the concrete implementation process of the IRIMER approach, and comparison analysis shows that the IRIMER approach is more flexible and effective than the RIMER [J. B. Yang, J. Liu, J. Wang, H. S. Sii and H. W. Wang, Belief rule-base interference methodology using the evidential reasoning approach-RIMER, IEEE Transaction on Systems Man and Cybernetics Part A-Systems and Humans36 (2006) 266–285.] approach and the ERIMER [J. Liu, L. Martínez, A. Calzada and H. Wang, A novel belief rule base representation, generation and its inference methodology, Knowledge-Based Systems 53 (2013) 129–141.] approach.

A neural mechanism of cognitive control for resolving conflict between abstract task rules

Conflict between multiple sensory stimuli or potential motor responses is thought to be resolved via bias signals from prefrontal cortex (PFC). However, population codes in the PFC also represent abstract information, such as task rules. How is conflict between active abstract representations resolved? We used functional neuroimaging to investigate the mechanism responsible for resolving conflict between abstract representations of task rules. Participants performed two different tasks based on a cue. We manipulated the degree of conflict at the task-rule level by training participants to associate the color and shape dimensions of the cue with either the same task rule (congruent cues) or different ones (incongruent cues). Phonological and semantic tasks were used in which performance depended on learned, abstract representations of information, rather than sensory features of the target stimulus or on any habituated stimulus-response associations. In addition, these tasks activate distinct regions that allowed us to measure magnitude of conflict between tasks. We found that incongruent cues were associated with increased activity in several cognitive control areas, including the inferior frontal gyrus, inferior parietal lobule, insula, and subcortical regions. Conflict between abstract representations appears to be resolved by rule-specific activity in the inferior frontal gyrus that is correlated with enhanced activity related to the relevant information. Furthermore, multi-voxel pattern analysis of the activity in the inferior frontal gyrus was shown to carry information about both the currently relevant rule (semantic/phonological) and the currently relevant cue context (color/shape). Similar to models of attentional selection of conflicting sensory or motor representations, the current findings indicate part of the frontal cortex provides a bias signal, representing task rules, that enhances task-relevant information. However, the frontal cortex can also be the target of these bias signals in order to enhance abstract representations that are independent of particular stimuli or motor responses.

Dimension of the Gibbs function topological manifold: 3. Configuration entropy determined by the isotopic composition of binary quasicrystals

Even though the graph representation of thermodynamic states rules out the existence of stable binary quasicrystals, Tsai’s research team was able to discover two such systems in 2000. Our previous article showed that in terms of isotopic composition, Tsai’s two quasicrystals, as well as several other ones that have been discovered since 2000, are pseudobinary. Such systems owe their stability to high configuration entropy, which is determined by their isotopic composition. In this article, we shall use the basic methods of statistical thermodynamics to show that configuration entropy for systems without symmetry (including translational symmetry) is always higher than the entropy of their symmetrical counterparts. Thermodynamic calculations for a model of a 1-D binary Fibonacci quasicrystal, \(\hbox {AB}_{\uptau }\), indicate that the internal energy of this system is the same as the internal energy of its approximant, \(\hbox {AB}_{\mathrm{t}}(t\approx \uptau \)), i.e., the crystalline counterpart of the quasicrystal. We shall show that assuming Element A in both systems is composed of two isotopes, the configuration entropy corresponding to a single atom of the quasicrystal is about 20 % higher than the entropy of its crystalline approximant. This article also includes calculations of the configuration entropies of the few pseudobinary quasicrystals that have been synthesised so far. These entropies are exceptionally high, most often amounting to about 1.5 \(k_{\mathrm{B}}\), due to a large number of isotopic components of these quasicrystals. It in unknown whether such a high value is sufficient to ensure the stability of the quasicrystalline phase compared to the crystalline phase, as the number of configurations of the approximant required for calculations is astronomical.

Self-similarity and recursion as default modes in human cognition

Humans generate recursive hierarchies in a variety of domains, including linguistic, social and visuo-spatial modalities. The ability to represent recursive structures has been hypothesized to increase the efficiency of hierarchical processing. Theoretical work together with recent empirical findings suggests that the ability to represent the self-similar structure of hierarchical recursive stimuli may be supported by internal neural representations that compress raw external information and increase efficiency.In order to explicitly test whether the representation of recursive hierarchies depends on internalized rules we compared the processing of visual hierarchies represented either as recursive or non-recursive, using task-free resting-state fMRI data. We aimed to evaluate the relationship between task-evoked functional networks induced by cognitive representations with the corresponding resting-state architecture. We observed increased connectivity within Default Mode Network (DMN) related brain areas during the representation of recursion, while non-recursive representations yielded increased connectivity within the Fronto-Parietal Control-Network.Our results suggest that human hierarchical information processing using recursion is supported by the DMN. In particular, the representation of recursion seems to constitute an internally-biased mode of information-processing that is mediated by both the core and dorsal-medial subsystems of the DMN. Compressed internal rule representations mediated by the DMN may help humans to represent and process hierarchical structures in complex environments by considerably reducing information processing load.

What the Darken–Gurry Plot Means About the Solubility of Elements in Metals

The solubility of elements in metals is a classical subject but still an important one. There are Hume-Rothery rules, which describe the solubility of elements in metals. The Darken–Gurry plot is a diagrammatical representation of Hume-Rothery rules in which the atomic radius is taken as the abscissa and the electronegativity as the ordinate. Apart from the existing methods, the author introduced the “thermal conductivity-Young’s modulus” (TC-YM) diagram in which thermal conductivity is taken as the abscissa and the Young’s modulus as the ordinate. The maximum solubilities of elements in γ-Fe, α-Fe, Cu, and Al are shown on both the Darken–Gurry plot and the TC-YM diagram. The TC-YM diagrams showed clear patterns of solubility on the diagram, while the Darken–Gurry plots showed only vague ones. According to the TC-YM diagram, the atomic radius and electronegativity, which are factors in the Darken–Gurry plot, were proven to be similar factors of the same trend. This is why the Darken–Gurry plot shows vague trends on the figure. On the other hand, the TC-YM diagram shows a clear trend of the solubility of elements. The same crystal structure, nearness to the matrix metal on the diagram, and similar Young’s modulus are concluded to be main factors for the large solubility.

Filozoficzne trudności teorii interesariuszy

Philosophical difficulties of stakeholder theory – which plays an important role in CSR and business ethics – are connected first of all with questions of its status and justification. What sense does stakeholder theory have: descriptive, instrumental or normative? And if normative, why then should executives worry about multiple stakeholder demands? It is well known that Freeman, one of the most important authors of stakeholder theory, deliberately disregarded these problems. In philosophical questions he invoked Rorty’s pragmatism, which in his opinion effectively undermined the “positivistic” dichotomy between facts and values, science and ethics, and enabled stakeholder theory to be understood at the same time as both descriptive and normative. The article presents some difficulties connected with this view, focusing on its dubious assumptions and unfavourable consequences. To the assumptions belongs a false dilemma taken from Rorty, which states that knowledge follows either a rule of representation or a rule of solidarity. One of the unfavourable consequences is the conclusion that stakeholder theory may be true only if its followers are able to force the stakeholders to accept its truthfulness. The main thesis of the article says that, as a result of pragmatic justification, stakeholder theory became a sort of arbitrary narration, which is unable to deal with its (empirical) misuses. However, a return to a more traditional view on facts and values enables us to appreciate the descriptive advantages of the theory and to identify difficulties connected with its normative layer. From this point of view, the attempt at a pragmatic interpretation of stakeholder theory was a misunderstanding that should be withdrawn from circulation.

Prediction of seasonal temperature using soft computing techniques: application in Benevento (Southern Italy) area

In this work two soft computing methods, Artificial Neural Networks and Genetic Programming, are proposed in order to forecast the mean temperature that will occur in future seasons. The area in which the soft computing techniques were applied is that of the surroundings of the town of Benevento, in the south of Italy, having geographic coordinates (lat. 41{\deg}07'50"N; long.14{\deg}47'13"E). This area is not affected by maritime influences as well as by winds coming from the west. The methods are fed by data recorded in the meteorological stations of Benevento and Castelvenere, located in the hilly area, which characterizes the territory surrounding this city, at 144 m a.s.l. Both the applied methods show low error rates, while the Genetic Programming offers an explicit rule representation (a formula) explaining the prevision. Keywords Seasonal Temperature Forecasting; Soft Computing; Artificial Neural Networks; Genetic Programming; Southern Italy.

Smooth criminal: convicted rule-breakers show reduced cognitive conflict during deliberate rule violations.

Classic findings on conformity and obedience document a strong and automatic drive of human agents to follow any type of rule or social norm. At the same time, most individuals tend to violate rules on occasion, and such deliberate rule violations have recently been shown to yield cognitive conflict for the rule-breaker. These findings indicate persistent difficulty to suppress the rule representation, even though rule violations were studied in a controlled experimental setting with neither gains nor possible sanctions for violators. In the current study, we validate these findings by showing that convicted criminals, i.e., individuals with a history of habitual and severe forms of rule violations, can free themselves from such cognitive conflict in a similarly controlled laboratory task. These findings support an emerging view that aims at understanding rule violations from the perspective of the violating agent rather than from the perspective of outside observer.

Dopaminergic Genetic Polymorphisms Predict Rule-based Category Learning.

Dopaminergic genes play an important role in cognitive function. DRD2 and DARPP-32 dopamine receptor gene polymorphisms affect striatal dopamine binding potential, and the Val158Met single-nucleotide polymorphism of the COMT gene moderates dopamine availability in the pFC. Our study assesses the role of these gene polymorphisms on performance in two rule-based category learning tasks. Participants completed unidimensional and conjunctive rule-based tasks. In the unidimensional task, a rule along a single stimulus dimension can be used to distinguish category members. In contrast, a conjunctive rule utilizes a combination of two dimensions to distinguish category members. DRD2 C957T TT homozygotes outperformed C allele carriers on both tasks, and DARPP-32 AA homozygotes outperformed G allele carriers on both tasks. However, we found an interaction between COMT and task type where Met allele carriers outperformed Val homozygotes in the conjunctive rule task, but both groups performed equally well in the unidimensional task. Thus, striatal dopamine binding may play a critical role in both types of rule-based tasks, whereas prefrontal dopamine binding is important for learning more complex conjunctive rule tasks. Modeling results suggest that striatal dopaminergic genes influence selective attention processes whereas cortical genes mediate the ability to update complex rule representations.

Dopamine D1 and D2 Receptors Make Dissociable Contributions to Dorsolateral Prefrontal Cortical Regulation of Rule-Guided Oculomotor Behavior

Studies of neuromodulation of spatial short-term memory have shown that dopamine D1 receptor (D1R) stimulation in dorsolateral prefrontal cortex (DLPFC) dose-dependently modulates memory activity, whereas D2 receptors (D2Rs) selectively modulate activity related to eye movements hypothesized to encode movement feedback. We examined localized stimulation of D1Rs and D2Rs on DLPFC neurons engaged in a task involving rule representation in memory to guide appropriate eye movements toward or away from a visual stimulus. We found dissociable effects of D1R and D2R on DLPFC physiology. D1R stimulation degrades memory activity for the task rule and increases stimulus-related selectivity. In contrast, D2R stimulation affects motor activity tuning only when eye movements are made to the stimulus. Only D1R stimulation degrades task performance and increases impulsive responding. Our results suggest that D1Rs regulate rule representation and impulse control, whereas D2Rs selectively modulate eye-movement-related dynamics and not rule representation in the DLPFC.

Virtual Camera Control Based on Cinematographic Rules of Camera Shot and Camera Angle

This research is focused on developing automatic virtual camera control in 3D video game to support a filmmaking based on machinima. Rule-based virtual camera control defines the camera position and the angle. A virtual camera is developed by representing camera shot and camera angle since it can control its position and rotation automatically. Knowledge acquisition for camera shot and camera angle is conducted by simulating virtual camera to the character based on rules of camera shot which are close-up, medium-shot, and long-shot, and rules of camera angle which are eye-level, low angle and high angle. The constrained areas are represented by defining paths. The paths have the function of track references to define position and rotation for the virtual camera, so the virtual camera can capture character vertically, horizontally, and diagonally. Data of constrained areas are representation of camera shot and camera angle rules. The data are used as knowledge base and rules in the term of perspective packages. The virtual camera is generated using Genetic algorithm by randomizing a number of position and rotation value as initial population, then the system calculates the value to find ideal virtual camera which can fulfil the rules in perspective packages. An implementation of the research is conducted by developing a game in which the automatic virtual camera is generated to produce a short animation with duration of 120 seconds. The rules of transition are implemented using different cut as type of transition in every 4 seconds, which means that there are 30 cut to produce. The result shows that the method used in this research reflects the expectation. All the 30 cuts produced in the game are captured using automatic virtual camera. With frame rate at 40-60 fps, the virtual camera is generated in 2 to 16 generations, with duration from 16 to 37 milliseconds.

Task Encoding across the Multiple Demand Cortex Is Consistent with a Frontoparietal and Cingulo-Opercular Dual Networks Distinction.

Multiple-demand (MD) regions of the human brain show coactivation during many different kinds of task performance. Previous work based on resting-state functional magnetic resonance imaging (fMRI) has shown that MD regions may be divided into two closely coupled subnetworks centered around the lateral frontoparietal (FP) and cingulo-opercular cortex. Here, we used on-task fMRI to test whether this division is apparent during the performance of an executive task. Furthermore, we investigated whether there is a difference in the encoding of task between the two subnetworks. Using connectivity methods, we found that activity across the entire MD cortex is correlated during task performance. Meanwhile, however, there was significantly stronger connectivity within each of the subnetworks than between them. Using multivoxel pattern analysis, we also found that, although we were able to decode task-relevant information from all regions of the MD cortex, classification accuracy scores were significantly higher in the FP subnetwork. These results suggest a nested picture with MD regions as a whole showing coactivation and broad rule representation, but with significant functional distinctions between component subnetworks. Multiple-demand (MD) regions of frontal and parietal cortex appear essential for the orchestration of goal-directed behavior and problem solving. Understanding the relative specialization of regions within the MD cortex is crucial to understanding how we can coordinate and execute complex action plans. By examining functional connectivity during task performance, we extend previous findings suggesting that the MD cortex can be divided into two subnetworks centered around the frontoparietal (FP) and cingulo-opercular (CO) cortex. Furthermore, using multivoxel pattern analysis, we show that, compared with the CO subnetwork, the FP subnetwork manifests more differentiated coding of specific task events.

Representation of replacement rules in the form of a matrix

Purpose – The purpose of this paper is to represent replacement policies (rules) in the form of a matrix. Visualization of replacement rules is useful for maintenance records. Matrix representation is more effective than the verbal description usually provided, as it allows better understanding of the specifics of the different replacement rules without careful research of their mathematical models. Design/methodology/approach – This approach employs mathematical models to investigate the simple conditions (requirements) for replacement of system component with illustrative examples. When comparing the different replacement rules a cost structure is applied to takes into account the nature and technology of disassembly assembly actions for the repair unit. Findings – Representation of replacement rules in the matrix form is useful when describing planned replacement models, opportunity replacement models, group replacement models and others, as well as computer modeling of the renewal process. Forming simple conditions for the replacement of system components ensures the total average repair cost is minimized. These conditions can be applied in the early stages of creating a maintenance program for the machine. Practical implications – Replacement matrices can be specified in a technical manual for maintenance of machines to achieve reliable operation and to reduce repair costs. Replacement matrices can be put into practical use for maintenance records and may be included in the maintenance procedures library of CMMSs. Developed in the paper, the replacement matrix, the conditions for replacement of system components and the cost structure will help engineers to make decisions at the time of repair for assembly units. Originality/value – Proposed in the paper is a new approach to the visualization of the replacement rules and cost structure which simplifies the analysis of options for repair actions. The proposed technique contributes to the record of maintenance actions and the decision making process for replacement.

Anxiety Evokes Hypofrontality and Disrupts Rule-Relevant Encoding by Dorsomedial Prefrontal Cortex Neurons.

A debilitating aspect of anxiety is its impact on decision making and flexible control of behavior. These cognitive constructs depend on proper functioning of the prefrontal cortex (PFC). Understanding how anxiety affects PFC encoding of cognitive events is of great clinical and evolutionary significance. Using a clinically valid experimental model, we find that, under anxiety, decision making may be skewed by salient and conflicting environmental stimuli at the expense of flexible top-down guided choices. We also find that anxiety suppresses spontaneous activity of PFC neurons, and weakens encoding of task rules by dorsomedial PFC neurons. These data provide a neuronal encoding scheme for how anxiety disengages PFC during decision making.

DUAL QUATERNIONIC REGULAR FUNCTION OF DUAL QUATERNION VARIABLES

We give representations of differential operators and rules for addition and multiplication of dual quaternions. Also, we research the notions and properties of a regular function and a corresponding harmonic function with values in dual quaternions of Clifford analysis.

Interactively recurrent fuzzy functions with multi objective learning and its application to chaotic time series prediction

Fuzzy functions (FFs) models were introduced as an alternate representation of the fuzzy rule based approaches. This paper presents novel Interactively Recurrent Fuzzy Functions (IRFFs) for nonlinear chaotic time series prediction. Chaotic sequences are strongly dependent on their initial conditions as well as past states, therefore feed forward FFs models cannot perform properly. To overcome this weakness, recurrent structure of FFs is proposed by placing local and global feedbacks in the output parts of multidimensional subspaces. IRFFs' optimized parameters should minimize the output error and maximize clusters density. To achieve these contradictory goals, Non-dominated Sorting Genetic Algorithm II (NSGAII) is applied for simultaneously optimizing the objectives. Also, feedback loop parameters are tuned by utilizing gradient descent algorithm with line search strategy based on the strong Wolfe condition. The experimental setup includes comparative studies on prediction of benchmark chaotic sequences and real lung sound data. Further simulations demonstrate that our proposed approach effectively learns complex temporal sequences and outperforms fuzzy rule based approaches and feed forward FFs.

Game level layout generation using evolved cellular automata

ABSTRACTDesign of level layouts typically involves the production of a set of levels which are different, yet display a consistent style based on the purpose of a particular level. In this paper, a new approach to the generation of unique level layouts, based on a target set of attributes, is presented. These attributes, which are learned automatically from an example layout, are used for the off-line evolution of a set of cellular automata rules. These rules can then be used for the real-time generation of level layouts that meet the target parameters. The approach is demonstrated on a set of maze-like level layouts. Results are presented to show the effect of various CA parameters and rule representation.