Load-Sharing Policies in Parallel Simulation of Agent-Based Demographic Models

Execution parallelism in agent-Based Simulation (ABS) allows to deal with complex/large-scale models. This raises the need for runtime environments able to fully exploit hardware parallelism, while jointly offering ABS-suited programming abstractions. In this paper, we target last-generation Parallel Discrete Event Simulation (PDES) platforms for multicore systems. We discuss a programming model to support both implicit (in-place access) and explicit (message passing) interactions across concurrent Logical Processes (LPs). We discuss different load-sharing policies combining event rate and implicit/explicit LPs’ interactions. We present a performance study conducted on a synthetic test case, representative of a class of agent-based models

Programming agent-based demographic models with cross-state and message-exchange dependencies: A study with speculative PDES and automatic load-sharing

Agent-based modeling and simulation is a versatile and promising methodology to capture complex interactions among entities and their surrounding environment. A great advantage is its ability to model phenomena at a macro scale by exploiting simpler descriptions at a micro level. It has been proven effective in many fields, and it is rapidly becoming a de-facto standard in the study of population dynamics. In this article we study programmability and performance aspects of the last-generation ROOT-Sim speculative PDES environment for multi/many-core shared-memory architectures. ROOT-Sim transparently offers a programming model where interactions can be based on both explicit message passing and in-place state accesses. We introduce programming guidelines for systematic exploitation of these facilities in agent-based simulations, and we study the effects on performance of an innovative load-sharing policy targeting these types of dependencies. An experimental assessment with synthetic and real-world applications is provided, to assess the validity of our proposal

Franco-Japanese Research Collaboration on Constraint Programming

International audienceConstraint programming is an emergent technology that allows modeling and solving various problems in many areas such as artificial intelligence, computer programming, computer-aided design, computer graphics, and user interfaces. In this report, we provide recent activities of research collaboration on constraint programming conducted by the authors and other researchers in France and Japan. First, we outline our joint research projects on constraint programming, and then present the backgrounds, goals, and approaches of several research topics treated in the projects. Second, we describe the two Franco-Japanese Workshops on Constraint Programming (FJCP), which we organized in Japan in October 2004 and in France in November 2005. We conclude with future prospects for collaboration between French and Japanese researchers in this area

A dynamic default revision mechanism for speculative computation

In this work a default revision mechanism is introduced into Speculative Computation to manage incomplete information. The default revision is supported by a method for the generation of default constraints based on Bayesian Networks. The method enables the generation of an initial set of defaults which is used to produce the most likely scenarios during the computation, represented by active processes. As facts arrive, the Bayesian Network is used to derive new defaults. The objective with such a new dynamic mechanism is to keep the active processes coherent with arrived facts. This is achieved by changing the initial set of default constraints during the reasoning process in Speculative Computation. A practical example in clinical decision support is described.info:eu-repo/semantics/publishedVersio

Speculative Computation with constraint processing for the generation of clinical scenarios

Clinical decision making often involves making decisions in situations of uncertainty. Clinical Decision Support Systems are tools devised to help in such moments, but the information may not be available during the decision process. Be it because of communication failure or errors in data input, the truth is that it would be beneficial to present the most likely clinical scenarios to a physician, given the incompleteness of the information. Speculative Computation offers a way to structure such a scenario generation process. This work presents a framework for clinical decision support with disjunctive constraint processing that acts as an interface with computer-interpretable versions of Clinical Practice Guidelines. Being a reasoning process based on defaults, it has to rely on a default generation process. For that we propose Bayesian Networks. The interaction between the different components of the system resulted in a process capable of generating clinical scenarios.This work is part-funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT ( Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-028980 (PTDC/EEI-SII/1386/2012) and project PEst-OE/EEI/UI0752/2014. The work of Tiago Oliveira is supported by a doctoral grant by FCT (SFRH/BD/85291/2012)

An Agent-Based Simulation API for Speculative PDES Runtime Environments

Agent-Based Modeling and Simulation (ABMS) is an effective paradigm to model systems exhibiting complex interactions, also with the goal of studying the emergent behavior of these systems. While ABMS has been effectively used in many disciplines, many successful models are still run only sequentially. Relying on simple and easy-to-use languages such as NetLogo limits the possibility to benefit from more effective runtime paradigms, such as speculative Parallel Discrete Event Simulation (PDES). In this paper, we discuss a semantically-rich API allowing to implement Agent-Based Models in a simple and effective way. We also describe the critical points which should be taken into account to implement this API in a speculative PDES environment, to scale up simulations on distributed massively-parallel clusters. We present an experimental assessment showing how our proposal allows to implement complicated interactions with a reduced complexity, while delivering a non-negligible performance increase

Inferring Interpersonal Relations in Narrative Summaries

Characterizing relationships between people is fundamental for the understanding of narratives. In this work, we address the problem of inferring the polarity of relationships between people in narrative summaries. We formulate the problem as a joint structured prediction for each narrative, and present a model that combines evidence from linguistic and semantic features, as well as features based on the structure of the social community in the text. We also provide a clustering-based approach that can exploit regularities in narrative types. e.g., learn an affinity for love-triangles in romantic stories. On a dataset of movie summaries from Wikipedia, our structured models provide more than a 30% error-reduction over a competitive baseline that considers pairs of characters in isolation

A reasoning module for distributed clinical decision support systems

One of the main challenges in distributed clinical decision support systems is to ensure that the flow of information is kept. The failure of one or more components should not bring down an entire system. Moreover, it should not impair any decision processes that are taking place in a functioning component. This work describes a decision module that is capable of managing states of incomplete information which result from the failure of communication between components or delays in making the information available. The framework is also capable of generating scenarios for situations in which there are information gaps. The proposal is described through an example about colon cancer staging.This work is part-funded by ERDF-European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT-Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-028980 and project scope UID/CEC/00319/2013. The work of Tiago Oliveira is supported by a FCT grant with the reference SFRH/BD/85291/2012.info:eu-repo/semantics/publishedVersio