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

An Expressive Language and Efficient Execution System for Software Agents

Software agents can be used to automate many of the tedious, time-consuming information processing tasks that humans currently have to complete manually. However, to do so, agent plans must be capable of representing the myriad of actions and control flows required to perform those tasks. In addition, since these tasks can require integrating multiple sources of remote information ? typically, a slow, I/O-bound process ? it is desirable to make execution as efficient as possible. To address both of these needs, we present a flexible software agent plan language and a highly parallel execution system that enable the efficient execution of expressive agent plans. The plan language allows complex tasks to be more easily expressed by providing a variety of operators for flexibly processing the data as well as supporting subplans (for modularity) and recursion (for indeterminate looping). The executor is based on a streaming dataflow model of execution to maximize the amount of operator and data parallelism possible at runtime. We have implemented both the language and executor in a system called THESEUS. Our results from testing THESEUS show that streaming dataflow execution can yield significant speedups over both traditional serial (von Neumann) as well as non-streaming dataflow-style execution that existing software and robot agent execution systems currently support. In addition, we show how plans written in the language we present can represent certain types of subtasks that cannot be accomplished using the languages supported by network query engines. Finally, we demonstrate that the increased expressivity of our plan language does not hamper performance; specifically, we show how data can be integrated from multiple remote sources just as efficiently using our architecture as is possible with a state-of-the-art streaming-dataflow network query engine

Rethinking affordance

n/a – Critical survey essay retheorising the concept of 'affordance' in digital media context. Lead article in a special issue on the topic, co-edited by the authors for the journal Media Theory

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)

Optimizing memory management for optimistic simulation with reinforcement learning

Simulation is a powerful technique to explore complex scenarios and analyze systems related to a wide range of disciplines. To allow for an efficient exploitation of the available computing power, speculative Time Warp-based Parallel Discrete Event Simulation is universally recognized as a viable solution. In this context, the rollback operation is a fundamental building block to support a correct execution even when causality inconsistencies are a posteriori materialized. If this operation is supported via checkpoint/restore strategies, memory management plays a fundamental role to ensure high performance of the simulation run. With few exceptions, adaptive protocols targeting memory management for Time Warp-based simulations have been mostly based on a pre-defined analytic models of the system, expressed as a closed-form functions that map system's state to control parameters. The underlying assumption is that the model itself is optimal. In this paper, we present an approach that exploits reinforcement learning techniques. Rather than assuming an optimal control strategy, we seek to find the optimal strategy through parameter exploration. A value function that captures the history of system feedback is used, and no a-priori knowledge of the system is required. An experimental assessment of the viability of our proposal is also provided for a mobile cellular system simulation

Speculative orientation and tracking system

The current progresses at the intersection of computer science and health care have the potential of greatly improving the living conditions of people with disabilities by removing obstacles that impair the normal unfolding of their everyday lives. Assistive technologies, as an application of scientific knowledge, aim to help users with their diminished capacities and, usually, imply a small adaptation from individuals so that they can use the devices that convey assistive functionalities. One of the most commonly diminished capabilities is that of spatial orientation. This is mirrored by several research works whose goal is to help human beings to travel between locations. Once set up, most of the systems featured in these research works requires changes in the configurations to be made manually in order to achieve a better adjustment to the user. In order to overcome this drawback, the work presented herein features a framework of Speculative Computation to set up the computation of the next step of a user using default values. The consequence of the application of the framework is a faster reaction to user stimuli, which may result in issuing warnings when he is likely to choose the wrong direction.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 Fundac¸ao para a Ci ˜ encia e a Tecnologia (Portuguese Foundation for Science and ˆ Technology) within project FCOMP-01-0124-FEDER-028980 (PTDC/EEI-SII/1386/2012). The work of Joao Ramos is supported by a doctoral grant by FCT - Fundac¸ ˜ ao para a Ci ˜ encia e a ˆ Tecnologia (Portuguese Foundation for Science and Technology) SFRH/BD/89530/2012. The work of Tiago Oliveira is also supported by the FCT grant with the reference SFRH/BD/85291/- 2012.info:eu-repo/semantics/publishedVersio

Decision Taking versus Action Determination

Decision taking is discussed in the context of the role it may play for various types of agents, and it is contrasted with action determination. Some remarks are made about the role of decision taking and action determination in the ongoing debate concerning the reverse polder development of the hertogin Hedwige polder