A Survey of Languages for Specifying Dynamics: A Knowledge Engineering Perspective

A number of formal specification languages for knowledge-based systems has been developed. Characteristics for knowledge-based systems are a complex knowledge base and an inference engine which uses this knowledge to solve a given problem. Specification languages for knowledge-based systems have to cover both aspects. They have to provide the means to specify a complex and large amount of knowledge and they have to provide the means to specify the dynamic reasoning behavior of a knowledge-based system. We focus on the second aspect. For this purpose, we survey existing approaches for specifying dynamic behavior in related areas of research. In fact, we have taken approaches for the specification of information systems (Language for Conceptual Modeling and TROLL), approaches for the specification of database updates and logic programming (Transaction Logic and Dynamic Database Logic) and the generic specification framework of abstract state machine

Behavior change interventions: the potential of ontologies for advancing science and practice

A central goal of behavioral medicine is the creation of evidence-based interventions for promoting behavior change. Scientific knowledge about behavior change could be more effectively accumulated using "ontologies." In information science, an ontology is a systematic method for articulating a "controlled vocabulary" of agreed-upon terms and their inter-relationships. It involves three core elements: (1) a controlled vocabulary specifying and defining existing classes; (2) specification of the inter-relationships between classes; and (3) codification in a computer-readable format to enable knowledge generation, organization, reuse, integration, and analysis. This paper introduces ontologies, provides a review of current efforts to create ontologies related to behavior change interventions and suggests future work. This paper was written by behavioral medicine and information science experts and was developed in partnership between the Society of Behavioral Medicine's Technology Special Interest Group (SIG) and the Theories and Techniques of Behavior Change Interventions SIG. In recent years significant progress has been made in the foundational work needed to develop ontologies of behavior change. Ontologies of behavior change could facilitate a transformation of behavioral science from a field in which data from different experiments are siloed into one in which data across experiments could be compared and/or integrated. This could facilitate new approaches to hypothesis generation and knowledge discovery in behavioral science

Towards Data-driven Simulation Modeling for Mobile Agent-based Systems

Simulation modeling provides insight into how dynamic systems work. Current simulation modeling approaches are primarily knowledge-driven, which involves a process of converting expert knowledge into models and simulating them to understand more about the system. Knowledge-driven models are useful for exploring the dynamics of systems, but are handcrafted which means that they are expensive to develop and reflect the bias and limited knowledge of their creators. To address limitations of knowledge-driven simulation modeling, this dissertation develops a framework towards data-driven simulation modeling that discovers simulation models in an automated way based on data or behavior patterns extracted from systems under study. By using data, simulation models can be discovered automatically and with less bias than through knowledge-driven methods. Additionally, multiple models can be discovered that replicate the desired behavior. Each of these models can be thought of as a hypothesis about how the real system generates the observed behavior. This framework was developed based on the application of mobile agent-based systems. The developed framework is composed of three components: 1) model space specification; 2) search method; and 3) framework measurement metrics. The model space specification provides a formal specification for the general model structure from which various models can be generated. The search method is used to efficiently search the model space for candidate models that exhibit desired behavior. The five framework measurement metrics: flexibility, comprehensibility, controllability, compossability, and robustness, are developed to evaluate the overall framework. Furthermore, to incorporate knowledge into the data-driven simulation modeling framework, a method was developed that uses System Entity Structures (SESs) to specify incomplete knowledge to be used by the model search process. This is significant because knowledge-driven modeling requires a complete understanding of a system before it can be modeled, whereas the framework can find a model with incomplete knowledge. The developed framework has been applied to mobile agent-based systems and the results demonstrate that it is possible to discover a variety of interesting models using the framework

Feature-Aware Verification

A software product line is a set of software products that are distinguished in terms of features (i.e., end-user--visible units of behavior). Feature interactions ---situations in which the combination of features leads to emergent and possibly critical behavior--- are a major source of failures in software product lines. We explore how feature-aware verification can improve the automatic detection of feature interactions in software product lines. Feature-aware verification uses product-line verification techniques and supports the specification of feature properties along with the features in separate and composable units. It integrates the technique of variability encoding to verify a product line without generating and checking a possibly exponential number of feature combinations. We developed the tool suite SPLverifier for feature-aware verification, which is based on standard model-checking technology. We applied it to an e-mail system that incorporates domain knowledge of AT&T. We found that feature interactions can be detected automatically based on specifications that have only feature-local knowledge, and that variability encoding significantly improves the verification performance when proving the absence of interactions.Comment: 12 pages, 9 figures, 1 tabl

On the convergence of autonomous agent communities

This is the post-print version of the final published paper that is available from the link below. Copyright @ 2010 IOS Press and the authors.Community is a common phenomenon in natural ecosystems, human societies as well as artificial multi-agent systems such as those in web and Internet based applications. In many self-organizing systems, communities are formed evolutionarily in a decentralized way through agents' autonomous behavior. This paper systematically investigates the properties of a variety of the self-organizing agent community systems by a formal qualitative approach and a quantitative experimental approach. The qualitative formal study by applying formal specification in SLABS and Scenario Calculus has proven that mature and optimal communities always form and become stable when agents behave based on the collective knowledge of the communities, whereas community formation does not always reach maturity and optimality if agents behave solely based on individual knowledge, and the communities are not always stable even if such a formation is achieved. The quantitative experimental study by simulation has shown that the convergence time of agent communities depends on several parameters of the system in certain complicated patterns, including the number of agents, the number of community organizers, the number of knowledge categories, and the size of the knowledge in each category

A graphical user interface for policy composition in CIM-SPL

CIM-SPL is a declarative policy specification language proposed inside DMTF. SPL policies allow the specification of rules to govern the behavior of a system using a PBM approach. However, SPL requires thorough knowledge of the language syntax as well as full understanding of the management scenario and its available management features. This paper describes a graphical CIM-SPL editor application and the supporting policy edition metaphor. A graphical composition process of SPL policies is proposed, based on the use of drag and drop operations of the policy component items in a graphical interface. The editor includes policy creation wizards that guide the user in the policy specification process, in order to alleviate network administrators from the difficulties associated with the intricacies of SPL language. Additionally, a text-based SPL edition tool is provided as a complement for experienced SPL language operators

On specification-based cyber-attack detection in smart grids

The transformation of power grids into intelligent cyber-physical systems brings numerous benefits, but also significantly increases the surface for cyber-attacks, demanding appropriate countermeasures. However, the development, validation, and testing of data-driven countermeasures against cyber-attacks, such as machine learning-based detection approaches, lack important data from real-world cyber incidents. Unlike attack data from real-world cyber incidents, infrastructure knowledge and standards are accessible through expert and domain knowledge. Our proposed approach uses domain knowledge to define the behavior of a smart grid under non-attack conditions and detect attack patterns and anomalies. Using a graph-based specification formalism, we combine cross-domain knowledge that enables the generation of whitelisting rules not only for statically defined protocol fields but also for communication flows and technical operation boundaries. Finally, we evaluate our specification-based intrusion detection system against various attack scenarios and assess detection quality and performance. In particular, we investigate a data manipulation attack in a future-orientated use case of an IEC 60870-based SCADA system that controls distributed energy resources in the distribution grid. Our approach can detect severe data manipulation attacks with high accuracy in a timely and reliable manner

Les antécédents de la relation acheteur-vendeur : le rôle médiateur du partage de la connaissance

This study aims to test the effects of buyer-seller relationship antecedents and test the mediating role of knowledge sharing. A quantitative study based on a sample of 100 marketing managers in small medium companies. Data were analyzed using the PLS Structural equation modeling. Findings show that Knowledge sharing, contract specification level and opportunistic behavior have a positive effect on buyer-seller relationship; however, power asymmetry has a negative effect. In addition, contract specification level, Dysfunctional conflict, opportunistic behavior were found to have a positive effect on knowledge sharing, however, power asymmetry has a negative effect. &nbsp