Learning to play Pac-Man: An evolutionary, rule-based approach

Pac-Man is a well-known, real-time computer game that provides an interesting platform for research. We describe an initial approach to developing an artificial agent that replaces the human to play a simplified version of Pac-Man. The agent is specified as a simple finite state machine and ruleset. with parameters that control the probability of movement by the agent given the constraints of the maze at some instant of time. In contrast to previous approaches, the agent represents a dynamic strategy for playing Pac-Man, rather than a pre-programmed maze-solving method. The agent adaptively "learns" through the application of population-based incremental learning (PBIL) to adjust the agents' parameters. Experimental results are presented that give insight into some of the complexities of the game, as well as highlighting the limitations and difficulties of the representation of the agent.

Formal considerations of rule-based messaging for Business Process Integration

In the last decade, with the expansion of organizational scope and the tendency for outsourcing, there has been an increasing need for Business Process Integration (BPI), understood as the sharing of data and applications among business processes. The research efforts and development paths in BPI pursued by many academic groups and system vendors, targeting heterogeneous system integration, continue to face several conceptual and technological challenges. This article begins with a brief review of major approaches and emerging standards to address BPI. Further, we introduce a rule-driven messaging approach to BPI, which is based on the harmonization of messages in order to compose a new, often cross-organizational process. We will then introduce the design of a temporal first order language (Harmonized Messaging Calculus) that provides the formal foundation for general rules governing the business process execution. Definitions of the language terms, formulae, safety, and expressiveness are introduced and considered in detail.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

Cue competition between elementary trained stimuli: US miscuing, interference, and US omission

The present series of experiments was designed to assess whether rule-based accounts of Pavlovian learning can account for cue competition effects observed after elemental training. All experiments involved initial differential conditioning training with A-US and B alone presentations. Miscuing refers to the fact that responding to A is impaired after one B-US presentation whereas interference is the impairment of responding to A after presentation of C-US pairings. Omission refers to the effects on B of A alone presentations. Experiments 1-2a provided clear evidence for miscuing whereas interference was not found after 1, 5 or 10 C-US pairings. Moreover, Experiments 3 and 3a found only weak evidence for interference in an A-US, B I C-US, D I A design used previously to show the effect. Experiments 4 and 5 failed to find any effect of US omission after one or five omission trials. The present results indicate that miscuing is more robust than is the interference effect. Moreover, the asymmetrical effects of US miscuing and US omission are difficult to accommodate within rule-based accounts of Pavlovian conditioning. (C) 2002 Elsevier Science (USA). All rights reserved.

Making distance learning E.R.O.T.I.C.: Applying interpretation principles to distance learning

Distance learners are self-directed learners traditionally taught via study books, collections of readings, and exercises to test understanding of learning packages. Despite advances in e-Learning environments and computer-based teaching interfaces, distance learners still lack opportunities to participate in exercises and debates available to classroom learners, particularly through non-text based learning techniques. Effective distance teaching requires flexible learning opportunities. Using arguments developed in interpretation literature, we argue that effective distance learning must also be Entertaining, Relevant, Organised, Thematic, Involving and Creative—E.R.O.T.I.C. (after Ham, 1992). We discuss an experiment undertaken with distance learners at The University of Queensland Gatton Campus, where we initiated an E.R.O.T.I.C. external teaching package aimed at engaging distance learners but using multimedia, including but not limited to text-based learning tools. Student responses to non-text media were positive.

Does industry matter? Examining the role of industry structure and organizational learning in innovation and brand performance

A manager's perception of industry structure (dynamism) has the potential to impact various organizational strategies and behaviors. This may be particularly so with regard to perceptions driving organizational learning orientations and innovation based marketing strategy. The position taken here suggests that firms operating within a competitive industry tend to pursue innovative ways of performing value-creating activities, which requires the development of learning capabilities. The results of a study of SMEs suggest that market focused learning, relative to other learning capabilities plays a key role in the relationships between industry structure, innovation and brand performance. The findings also show that market focused learning and internally focused learning influence innovation and that innovation influences a brand's performance. (c) 2005 Elsevier Inc. All rights reserved.

Contextual Deliberation of Cognitive Agents in Defeasible Logic

This article extends Defeasible Logic to deal with the contextual deliberation process of cognitive agents. First, we introduce meta-rules to reason with rules. Meta-rules are rules that have as a consequent rules for motivational components, such as obligations, intentions and desires. In other words, they include nested rules. Second, we introduce explicit preferences among rules. They deal with complex structures where nested rules can be involved.

Modelling pre-clearing vegetation distribution using GIS-integrated statistical, ecological and data models: A case study from the wet tropics of Northeastern Australia

Traditional vegetation mapping methods use high cost, labour-intensive aerial photography interpretation. This approach can be subjective and is limited by factors such as the extent of remnant vegetation, and the differing scale and quality of aerial photography over time. An alternative approach is proposed which integrates a data model, a statistical model and an ecological model using sophisticated Geographic Information Systems (GIS) techniques and rule-based systems to support fine-scale vegetation community modelling. This approach is based on a more realistic representation of vegetation patterns with transitional gradients from one vegetation community to another. Arbitrary, though often unrealistic, sharp boundaries can be imposed on the model by the application of statistical methods. This GIS-integrated multivariate approach is applied to the problem of vegetation mapping in the complex vegetation communities of the Innisfail Lowlands in the Wet Tropics bioregion of Northeastern Australia. The paper presents the full cycle of this vegetation modelling approach including sampling sites, variable selection, model selection, model implementation, internal model assessment, model prediction assessments, models integration of discrete vegetation community models to generate a composite pre-clearing vegetation map, independent data set model validation and model prediction's scale assessments. An accurate pre-clearing vegetation map of the Innisfail Lowlands was generated (0.83r(2)) through GIS integration of 28 separate statistical models. This modelling approach has good potential for wider application, including provision of. vital information for conservation planning and management; a scientific basis for rehabilitation of disturbed and cleared areas; a viable method for the production of adequate vegetation maps for conservation and forestry planning of poorly-studied areas. (c) 2006 Elsevier B.V. All rights reserved.

Dynamic response: Real-time adaptation for music emotion

Music plays an enormous role in today's computer games; it serves to elicit emotion, generate interest and convey important information. Traditional gaming music is fixed at the event level, where tracks loop until a state change is triggered. This behaviour however does not reflect musically the in-game state between these events. We propose a dynamic music environment, where music tracks adjust in real-time to the emotion of the in-game state. We are looking to improve the affective response to symbolic music through the modification of structural and performative characteristics through the application of rule-based techniques. In this paper we undertake a multidiscipline approach, and present a series of primary music-emotion structural rules for implementation. The validity of these rules was tested in small study involving eleven participants, each listening to six permutations from two musical works. Preliminary results indicate that the environment was generally successful in influencing the emotion of the musical works for three of the intended four directions (happier, sadder & content/dreamier). Our secondary aim of establishing that the use of music-emotion rules, sourced predominantly from Western classical music, could be applied with comparable results to modern computer gaming music was also largely successfully.

Influencing the perceived emotions of music with intent

Music is an immensely powerful affective medium that pervades our everyday life. With ever advancing technology, the reproduction and application of music for emotive and information transfer purposes has never been more prevalent. In this paper we introduce a rule-based engine for influencing the perceived emotions of music. Based on empirical music psychology, we attempt to formalise the relationship between musical elements and their perceived emotion. We examine the modification to structural aspects of music to allow for a graduated transition between perceived emotive states. This engine is intended to provide music reproduction systems with a finer grained control over this affective medium; where perceived musical emotion can be influenced with intent. This intent comes from both an external application and the audience. Using a series of affective computing technologies, an audience’s response metrics and attitudes can be incorporated to model this intent. A generative feedback loop is set up between the external application, the influencing process and the audience’s response to this, which together shape the modification of musical structure. The effectiveness of our rule system for influencing perceived musical emotion was examined in earlier work, with a small test study providing generally encouraging results.

Incremental model transformation for the evolution of model-driven systems

Model transformations are an integral part of model-driven development. Incremental updates are a key execution scenario for transformations in model-based systems, and are especially important for the evolution of such systems. This paper presents a strategy for the incremental maintenance of declarative, rule-based transformation executions. The strategy involves recording dependencies of the transformation execution on information from source models and from the transformation definition. Changes to the source models or the transformation itself can then be directly mapped to their effects on transformation execution, allowing changes to target models to be computed efficiently. This particular approach has many benefits. It supports changes to both source models and transformation definitions, it can be applied to incomplete transformation executions, and a priori knowledge of volatility can be used to further increase the efficiency of change propagation.

Compositional verification for object-Z

This paper presents a framework for compositional verification of Object-Z specifications. Its key feature is a proof rule based on decomposition of hierarchical Object-Z models. For each component in the hierarchy local properties are proven in a single proof step. However, we do not consider components in isolation. Instead, components are envisaged in the context of the referencing super-component and proof steps involve assumptions on properties of the sub-components. The framework is defined for Linear Temporal Logic (LTL)

Playing in continuous spaces: Some analysis and extension of population-based incremental learning

As an alternative to traditional evolutionary algorithms (EAs), population-based incremental learning (PBIL) maintains a probabilistic model of the best individual(s). Originally, PBIL was applied in binary search spaces. Recently, some work has been done to extend it to continuous spaces. In this paper, we review two such extensions of PBIL. An improved version of the PBIL based on Gaussian model is proposed that combines two main features: a new updating rule that takes into account all the individuals and their fitness values and a self-adaptive learning rate parameter. Furthermore, a new continuous PBIL employing a histogram probabilistic model is proposed. Some experiments results are presented that highlight the features of the new algorithms.