Representation Of Rules Research Articles

Why Are Rule Representations Difficult to Form ?

Why Are Rule Representations Difficult to Form ?

When words lead to solutions: executive function deficits in preschool children with specific language impairment.

Several studies indicate that school-age children with specific language impairment (SLI) have difficulties with tasks that rely on executive functions. Whether executive function deficits in children with SLI emerge during preschool age remains unclear. Our aim was to fill this gap by investigating executive function performances in two age groups of preschoolers with and without SLI. Children with SLI (N=60; young: 53.6±5.3 months; old: 65.4±3.8 months) and age-matched control children (N=58) were tested for problem-representation ability, using the Flexible Item Selection Task (FIST), rule-use skills, using a Stroop-like Day-Night test (D/N), and planning skills, using the Tower of London test (TOL). Older children performed better than younger children did across tasks. Children with SLI had poorer performance, compared to typically developing children, on measures of problem representation, planning skills, and use of rules. Our results clearly indicate that executive function impairment is evident during the preschool period. Although old children with SLI performed better than young children with SLI, their performances were still poor, compared to those of control peers. These findings suggest that children with SLI have altered executive functioning at 53.6 months.

Philosophy of Skillful Coping. Motor Intentionality vs. Representations for Action

In many of his papers the contemporary and much debated author Hubert Dreyfus resorts to Merleau-Ponty's concept of “motor intentionality” in order to uphold the view of direct relation, not mediated by mental representations, between subject and world. He claims that as a person becomes an expert in a domain (driving, playing piano etc.), she does not respond in a rule-like way to objects, but in a flexible way to situations as wholes. No representations of objects or rules are active anymore; the whole situation in which that person is immersed requires her to act in a certain way. However, skillful coping, although not guided by rules, is not automatic action; it is intentional behavior, viz. motor intentionality–the body's adaptation to the environmental “solicitations”. This paper challenges this thesis by arguing that, in order to account for skillful coping, one need to explain how the body succeeds in dealing with complicated objects that require multiple object tracking, specific timing and spatiotemporal coordination of movements. Using contemporary research findings, it could be argued that such coping needs binding of many kinds of information (about object features, spatial details and motor commands) in the same mental file. After clarifying the concept of “mental representation”, the paper argues that such files are subpersonal representations for action.

Hyper-heuristic Evolution of Dispatching Rules: A Comparison of Rule Representations.

Dispatching rules are frequently used for real-time, online scheduling in complex manufacturing systems. Design of such rules is usually done by experts in a time consuming trial-and-error process. Recently, evolutionary algorithms have been proposed to automate the design process. There are several possibilities to represent rules for this hyper-heuristic search. Because the representation determines the search neighborhood and the complexity of the rules that can be evolved, a suitable choice of representation is key for a successful evolutionary algorithm. In this paper we empirically compare three different representations, both numeric and symbolic, for automated rule design: A linear combination of attributes, a representation based on artificial neural networks, and a tree representation. Using appropriate evolutionary algorithms (CMA-ES for the neural network and linear representations, genetic programming for the tree representation), we empirically investigate the suitability of each representation in a dynamic stochastic job shop scenario. We also examine the robustness of the evolved dispatching rules against variations in the underlying job shop scenario, and visualize what the rules do, in order to get an intuitive understanding of their inner workings. Results indicate that the tree representation using an improved version of genetic programming gives the best results if many candidate rules can be evaluated, closely followed by the neural network representation that already leads to good results for small to moderate computational budgets. The linear representation is found to be competitive only for extremely small computational budgets.

Rule Representation for Nursing-Care Process Evaluation Using Decision Tree Techniques

Improving the quality of nursing care is crucial to maintaining the quality of life. Our objective is to develop a computer-aided evaluation system that enables nursing experts to improve the quality of nursing care. In our previous works, some classification systems based on fuzzy logic, neural networks, and SVMs were developed. Although a classification system with high performance for all nursing-care datasets is desirable, we focus on how to visualize the classification results in this paper. It is important to visualize the results for our nursing-care text classification system because the computer-aided system has to explain the reasons for obtaining such results to human experts. In this paper, a tree-type expression is considered for visualizing the classification results. To visualize classification results with the tree-type expression, we consider a decision tree technique. Word existence, dependency relations, and phrase-based feature vector definitions have been proposed in our previous works. In the present study, these three types of feature vector definitions are compared with one another from the viewpoint of understandability.

UIMA Ruta: Rapid development of rule-based information extraction applications

AbstractRule-based information extraction is an important approach for processing the increasingly available amount of unstructured data. The manual creation of rule-based applications is a time-consuming and tedious task, which requires qualified knowledge engineers. The costs of this process can be reduced by providing a suitable rule language and extensive tooling support. This paper presents UIMA Ruta, a tool for rule-based information extraction and text processing applications. The system was designed with focus on rapid development. The rule language and its matching paradigm facilitate the quick specification of comprehensible extraction knowledge. They support a compact representation while still providing a high level of expressiveness. These advantages are supplemented by the development environment UIMA Ruta Workbench. It provides, in addition to extensive editing support, essential assistance for explanation of rule execution, introspection, automatic validation, and rule induction. UIMA Ruta is a useful tool for academia and industry due to its open source license. We compare UIMA Ruta to related rule-based systems especially concerning the compactness of the rule representation, the expressiveness, and the provided tooling support. The competitiveness of the runtime performance is shown in relation to a popular and freely-available system. A selection of case studies implemented with UIMA Ruta illustrates the usefulness of the system in real-world scenarios.

The Comprehensive Mapping of Semantics of Business Vocabulary and Business Rules (SBVR) to OWL 2 Ontologies

The goal of the paper is to analyse the subset of Semantics of Business Vocabulary and Business Rules (SBVR) for a comprehensive representation of ontological knowledge defined using the Web Ontology Language OWL 2. SBVR is the OMG metamodel, which separates the representation and meaning of business concepts and business rules, and makes them understandable for business experts as well as for software systems. The SBVR can act as an interface between business participants and semantic technologies, such as OWL 2 that has developed means for describing ontological data and reasoning with them. SBVR provides the richer model for knowledge representation than OWL 2. Though there are a few proposals that have shown that it is possible to transform the significant subset of SBVR concepts into OWL 2 ontology, the suitability of SBVR to represent OWL 2 ontologies has not been studied in detail. The paper addresses the mentioned issue with regards to the transformation from SBVR into OWL 2. DOI: http://dx.doi.org/10.5755/j01.itc.43.3.6651

The conforming brain and deontological resolve.

Our personal values are subject to forces of social influence. Deontological resolve captures how strongly one relies on absolute rules of right and wrong in the representation of one's personal values and may predict willingness to modify one's values in the presence of social influence. Using fMRI, we found that a neurobiological metric for deontological resolve based on relative activity in the ventrolateral prefrontal cortex (VLPFC) during the passive processing of sacred values predicted individual differences in conformity. Individuals with stronger deontological resolve, as measured by greater VLPFC activity, displayed lower levels of conformity. We also tested whether responsiveness to social reward, as measured by ventral striatal activity during social feedback, predicted variability in conformist behavior across individuals but found no significant relationship. From these results we conclude that unwillingness to conform to others' values is associated with a strong neurobiological representation of social rules.

The “tweaking principle” for task switching

A hallmark of executive control is the brain’s agility to shift between different tasks depending on the behavioral rule currently in play [1]. Humans and other animals exhibit a remarkable ability to flexibly select an appropriate response to a sensory input, and rapidly switch to another sensory-response mapping when task rule or goal changes. An increasing number of monkey experiments have been performed using task-switching paradigms, combined with single-neuron recording from sensory, parietal, and prefrontal cortical areas. Physiological evidence from these studies suggests that modulation of neural activity by task rule is typically weak [2]. By contrast, most previous models commonly assume that a rule signal is similarly strong as sensory stimulation in affecting activity of cortical neurons [3]. How can small rule modulation explain large (binary) behavioral changes in task switching? In this work, we propose a solution to this puzzle, which we refer to as “the tweaking hypothesis” [4]. The core idea is that network reconfiguration underlying task switching can be realized by very weak top-down signals from rule neurons in prefrontal cortex. This is because a weak input can be greatly amplified through reverberating “attractor” dynamics in categorization and decision circuits, ultimately leading to circuit selection in favor of one sensory-motor mapping over another. We tested the tweaking hypothesis by developing a neural circuit model for task switching that consists of several basic and interacting circuit modules for sensory coding, rule representation, categorization of stimulus features, and action selection, respectively [4]. The model was validated by reproducing salient single-neuron physiological observations [2] and behavioral effects associated with task switching [1,5,6]. Notably, the model identifies specific circuit mechanisms, in terms of neural dynamics and reward-dependent synaptic plasticity, that explain salient and widely observed behavioral effects associated with task switching [4]: (i) Switch cost: response time and error rate increase in trials following a task switch. Switch cost splits into a component that decreases with a longer time for preparation and a residual component that remains [5]. (ii) Task-response interaction: on task repeat trials, the response time is shorter if the same motor response is repeated; by contrast on switch trials, response time is shorter if an alternative motor response is selected [5,6]. (iii) Congruency effect: response times and the error rate are larger when the stimulus is incongruent compared to when it is congruent, which depends on whether the mapped behavioral response is different or the same, according to alternative rules [5,6]. This work represents a neural circuit model for task switching and sheds insights in the brain mechanism of a fundamental cognitive capability; in particular, that category-selective neurons play an essential role in resolving the sensory-motor conflicts that typically appear in task-switching paradigms [4].

Markov-CA model using analytical hierarchy process and multiregression technique

The unprecedented increase in population and rapid rate of urbanisation has led to extensive land use changes. Cellular automata (CA) are increasingly used to simulate a variety of urban dynamics. This paper introduces a new CA based on an integration model built-in multi regression and multi-criteria evaluation to improve the representation of CA transition rule. This multi-criteria evaluation is implemented by utilising data relating to the environmental and socioeconomic factors in the study area in order to produce suitability maps (SMs) using an analytical hierarchical process, which is a well-known method. Before being integrated to generate suitability maps for the periods from 1984 to 2010 based on the different decision makings, which have become conditioned for the next step of CA generation. The suitability maps are compared in order to find the best maps based on the values of the root equation (R2). This comparison can help the stakeholders make better decisions. Thus, the resultant suitability map derives a predefined transition rule for the last step for CA model. The approach used in this study highlights a mechanism for monitoring and evaluating land-use and land-cover changes in Kirkuk city, Iraq owing changes in the structures of governments, wars, and an economic blockade over the past decades. The present study asserts the high applicability and flexibility of Markov-CA model. The results have shown that the model and its interrelated concepts are performing rather well.

Behavioral and neural correlates of executive functioning in musicians and non-musicians.

Executive functions (EF) are cognitive capacities that allow for planned, controlled behavior and strongly correlate with academic abilities. Several extracurricular activities have been shown to improve EF, however, the relationship between musical training and EF remains unclear due to methodological limitations in previous studies. To explore this further, two experiments were performed; one with 30 adults with and without musical training and one with 27 musically trained and untrained children (matched for general cognitive abilities and socioeconomic variables) with a standardized EF battery. Furthermore, the neural correlates of EF skills in musically trained and untrained children were investigated using fMRI. Adult musicians compared to non-musicians showed enhanced performance on measures of cognitive flexibility, working memory, and verbal fluency. Musically trained children showed enhanced performance on measures of verbal fluency and processing speed, and significantly greater activation in pre-SMA/SMA and right VLPFC during rule representation and task-switching compared to musically untrained children. Overall, musicians show enhanced performance on several constructs of EF, and musically trained children further show heightened brain activation in traditional EF regions during task-switching. These results support the working hypothesis that musical training may promote the development and maintenance of certain EF skills, which could mediate the previously reported links between musical training and enhanced cognitive skills and academic achievement.

Timing matters: the processing of pitch relations.

The human central auditory system can automatically extract abstract regularities from a variant auditory input. To this end, temporarily separated events need to be related. This study tested whether the timing between events, falling either within or outside the temporal window of integration (~350 ms), impacts the extraction of abstract feature relations. We utilized tone pairs for which tones within but not across pairs revealed a constant pitch relation (e.g., pitch of second tone of a pair higher than pitch of first tone, while absolute pitch values varied across pairs). We measured the mismatch negativity (MMN; the brain’s error signal to auditory regularity violations) to second tones that rarely violated the pitch relation (e.g., pitch of second tone lower). A Short condition in which tone duration (90 ms) and stimulus onset asynchrony between the tones of a pair were short (110 ms) was compared to two conditions, where this onset asynchrony was long (510 ms). In the Long Gap condition, the tone durations were identical to Short (90 ms), but the silent interval was prolonged by 400 ms. In Long Tone, the duration of the first tone was prolonged by 400 ms, while the silent interval was comparable to Short (20 ms). Results show a frontocentral MMN of comparable amplitude in all conditions. Thus, abstract pitch relations can be extracted even when the within-pair timing exceeds the integration period. Source analyses indicate MMN generators in the supratemporal cortex. Interestingly, they were located more anterior in Long Gap than in Short and Long Tone. Moreover, frontal generator activity was found for Long Gap and Long Tone. Thus, the way in which the system automatically registers irregular abstract pitch relations depends on the timing of the events to be linked. Pending that the current MMN data mirror established abstract rule representations coding the regular pitch relation, neural processes building these templates vary with timing.

Building tasks from verbal instructions: an EEG study on practice trial exposure and task structure complexity during novel sequences of behavior.

Configuring the mind to perform a novel task is an effortful process and one that is related to differences in general intelligence. Previous research has suggested that when participants are given instructions for a future task, representations of the rules contained in the instructions can influence subsequent behavior, even when the rules are not necessary to perform the upcoming task. One hypothesis for the continued activation of rule representations suggests that the practice trials participants perform before the experimental trials may instantiate the unnecessary task rules into participants' mental model of the task (i.e., the task space). To test this hypothesis, EEGs were recorded as participants (N = 66) completed a multirule task designed to contrast the effects of increasing task structure complexity and practice trial exposure. The results showed that, as was predicted, performance is significantly poorer when more task rules are specified in the task instructions. Practice trials with the extra rule did not affect task performance, indicating that an unacted verbal instruction is sufficient to incorporate the rule into participants' mental model of the task. The EEG results showed that instruction complexity was linked to a phasic increase in frontal theta synchronization but reduced posterior alpha and beta desynchronization. These changes in synchronization occurred during a time period of low intertrial phase coherence and suggest that participants were "checking the task rules" amidst a trial. This transient neural activity may reflect compensatory mechanisms for dealing with increased mind-wandering that is more likely to occur in complex tasks.

Coping with confounds in multivoxel pattern analysis: What should we do about reaction time differences? A comment on Todd, Nystrom & Cohen 2013

Multivoxel pattern analysis (MVPA) is a sensitive and increasingly popular method for examining differences between neural activation patterns that cannot be detected using classical mass-univariate analysis. Recently, Todd et al. (“Confounds in multivariate pattern analysis: Theory and rule representation case study”, 2013, NeuroImage 77: 157–165) highlighted a potential problem for these methods: high sensitivity to confounds at the level of individual participants due to the use of directionless summary statistics. Unlike traditional mass-univariate analyses where confounding activation differences in opposite directions tend to approximately average out at group level, group level MVPA results may be driven by any activation differences that can be discriminated in individual participants. In Todd et al.'s empirical data, factoring out differences in reaction time (RT) reduced a classifier's ability to distinguish patterns of activation pertaining to two task rules. This raises two significant questions for the field: to what extent have previous multivoxel discriminations in the literature been driven by RT differences, and by what methods should future studies take RT and other confounds into account? We build on the work of Todd et al. and compare two different approaches to remove the effect of RT in MVPA. We show that in our empirical data, in contrast to that of Todd et al., the effect of RT on rule decoding is negligible, and results were not affected by the specific details of RT modelling. We discuss the meaning of and sensitivity for confounds in traditional and multivoxel approaches to fMRI analysis. We observe that the increased sensitivity of MVPA comes at a price of reduced specificity, meaning that these methods in particular call for careful consideration of what differs between our conditions of interest. We conclude that the additional complexity of the experimental design, analysis and interpretation needed for MVPA is still not a reason to favour a less sensitive approach.

Preschoolers can form abstract rule representations regardless of cognitive flexibility

The abstractness of rule representations in the pre-switch phase of the Dimensional Change Card Sorting (DCCS) task was studied by letting 3- and 4-year-old children perform a standard DCCS task and a separate generalization task. In the generalization task, children were asked to generalize their sorting rules to novel stimuli in one of three conditions. In the relevant change condition, values of the relevant dimension changed; in the irrelevant change condition, values of the irrelevant dimension changed; and in the total change condition, values of both dimensions changed. All children showed high performance on the generalization task in the relevant change condition, implying an abstract rule representation at the level of dimensions (“same colors go together”). Performance in the relevant change condition was significantly better (and faster) than performance in the other two conditions. Children with high cognitive flexibility (switchers on the DCCS task) more often switched their attention to the irrelevant dimension in the generalization task only if values of the irrelevant dimension changed. Children with low cognitive flexibility (perseverators) were more often inconsistent in their sorting on the generalization task if values of both dimensions changed. The difference in performance on the DCCS task between switchers and perseverators seems to result from the processes that operate on the learned sorting rules and not from the abstractness of the rule representations children have.

Boundary Cutting for Packet Classification

Decision-tree-based packet classification algorithms such as HiCuts, HyperCuts, and EffiCuts show excellent search performance by exploiting the geometrical representation of rules in a classifier and searching for a geometric subspace to which each input packet belongs. However, decision tree algorithms involve complicated heuristics for determining the field and number of cuts. Moreover, fixed interval-based cutting not relating to the actual space that each rule covers is ineffective and results in a huge storage requirement. A new efficient packet classification algorithm using boundary cutting is proposed in this paper. The proposed algorithm finds out the space that each rule covers and performs the cutting according to the space boundary. Hence, the cutting in the proposed algorithm is deterministic rather than involving the complicated heuristics, and it is more effective in providing improved search performance and more efficient in memory requirement. For rule sets with 1000-100 000 rules, simulation results show that the proposed boundary cutting algorithm provides a packet classification through 10-23 on-chip memory accesses and 1-4 off-chip memory accesses in average.

Examining Two Different Approaches to Modelling Management and Operation Rules in a Single River System Model

Different water agencies use different modeling tools for water resources planning and management. For example, different jurisdictions in Australia use a variety of river system models and these models vary considerably in approach and assumptions, including different time steps (monthly and daily), flow routing (different types of routing and no routing), ordering solution (optimization and heuristic) and representation of management and operational rules. These fundamental differences in approach make it difficult to integrate existing models of connected river systems at a basin scale. A collaborative joint venture (the eWater CRC) between research, industry and government partners has recently developed an integrated river system modelling tool called “eWater Source” to improve on the existing river system models in Australia. One of the major advances in Source is the implementation of two decision algorithms, heuristic and NetLP approaches, for water distribution modelling in the same modelling platform. This paper describes the implementation of heuristic and NetLP approaches for water delivery under management and operations rules in Source, and compares the performance of the two approaches through a case study in the Goulburn-Broken-Campaspe-Loddon (GBCL) river system in south-eastern Australia. The key performance measures used to compare the approaches include the efficiency and equity of water delivery to water users, impacts on the reliability of supply, agreement with storage operating targets, and model application run time. The results demonstrate that the heuristic approach implemented in Source can replicate the performance of the NetLP approach for a model of reasonable complexity. This is important because the run times of models with heuristic approaches are shorter than models with NetLP approaches, so this will allow more complexity to be represented than was previously practical in equivalent NetLP applications. Agreement between jurisdictions to move to the single river system modelling platform will contribute to overcoming the problems faced by river managers in Australia in transboundary river basins.

Mortality, fertility, and the OY ratio in a model hunter–gatherer system

An agent-based model (ABM) is used to explore how the ratio of old to young adults (the OY ratio) in a sample of dead individuals is related to aspects of mortality, fertility, and longevity experienced by the living population from which the sample was drawn. The ABM features representations of rules, behaviors, and constraints that affect person- and household-level decisions about marriage, reproduction, and infant mortality in hunter-gatherer systems. The demographic characteristics of the larger model system emerge through human-level interactions playing out in the context of "global" parameters that can be adjusted to produce a range of mortality and fertility conditions. Model data show a relationship between the OY ratios of living populations (the living OY ratio) and assemblages of dead individuals drawn from those populations (the dead OY ratio) that is consistent with that from empirically known ethnographic hunter-gatherer cases. The dead OY ratio is clearly related to the mean ages, mean adult mortality rates, and mean total fertility rates experienced by living populations in the model. Sample size exerts a strong effect on the accuracy with which the calculated dead OY ratio reflects the actual dead OY ratio of the complete assemblage. These results demonstrate that the dead OY ratio is a potentially useful metric for paleodemographic analysis of changes in mortality and mean age, and suggest that, in general, hunter-gatherer populations with higher mortality, higher fertility, and lower mean ages are characterized by lower dead OY ratios.

An effective association rule mining scheme using a new generic basis

Association rule mining among itemsets is a fundamental task and is of great importance in many data mining applications including attacks in network data, stock market, financial applications, bioinformatics to find genetic disorders, etc. However, association rule extraction from a reasonable-sized database produces a large number of rules. As a result, many of them are redundant to other rules, and they are practically useless. To overcome this issue, methods for mining non-redundant rules are essentially required. To address such problem, we initially propose a definition for redundancy in sense of minimal knowledge and then a compact representation of non-redundant association rules which we call as compact informative generic basis. We also provide an improved version of the existing DCI_CLOSED algorithm (DCI_PLUS) to find out the frequent closed itemsets (FCI) with their minimal representative generators in combination with BitTable which represents a compact database form in a single scan of the original database. We further introduce an algorithm for constructing the compact informative generic basis from the FCI and their generators in an efficient way. We finally present an inference mechanism in which all association rules can be generated without accessing the database. Experiments are performed on the proposed method. The experimental results show that the proposed method outperforms the other existing related methods.

Modelling Land-use and Land-cover Changes Using Markov-CA, and Multiple Decision Making in Kirkuk City

This paper tries to highlight on the analysis of the land-use and land-cover changes in Kirkuk city / Iraq that's due to repeated changes in structures of governments, wars and economic blockade over the past three decades that drove to chaos. The Markov-cellular automata model is an important and powerful tool in simulation. In this article a new Markov-CA model was developed based on built-in multi-regression model and multi-criteria evaluation approach to improve the representation of Markov-CA transition rules. Utilised data relate to the environmental and socio-economic are produce criteria layers and weights in order to output the suitability maps via a ranking method for periods 1984, 1990, 2000 and 2010, which have become conditional for the next step of Markov-CA generation. The suitability maps are compared in order to determine the best fit maps based on the values of the root equation (R 2 ). This comparison helpful to the stakeholders make better decisions and the best maps indicated best transition rules, which is represent the core of Markov-CA model. Thus, the resultant of best suitability maps derives a predefined transition rule for the end of Markov-CA model step. The approach used in the present study a mechanism for monitoring the land-use and land-cover changes in Kirkuk depend on Markov-CA is implemented and evaluated thru the different decision maker. The results assert that the model bears a high applicability and flexibility degree in land-use and land-cover changes.