1,898 research outputs found
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An agent-based DDM for high level architecture
The Data Distribution Management (DDM) service is one of the six services provided in the Runtime Infrastructure (RTI) of High Level Architecture (HLA). Its purpose is to perform data filtering and reduce irrelevant data communicated between federates. The two DDM schemes proposed for RTI, region based and grid based DDM, are oriented to send as little irrelevant data to subscribers as possible, but only manage to filter part of this information and some irrelevant data is still being communicated. Previously (G. Tan et al., 2000), we employed intelligent agents to perform data filtering in HLA, implemented an agent based DDM in RTI (ARTI) and compared it with the other two filtering mechanisms. The paper reports on additional experiments, results and analysis using two scenarios: the AWACS sensing aircraft simulation and the air traffic control simulation scenario. Experimental results show that compared with other mechanisms, the agent based approach communicates only relevant data and minimizes network communication, and is also comparable in terms of time efficiency. Some guidelines on when the agent based scheme can be used are also give
A canonical theory of dynamic decision-making
Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering
Parallel Sort-Based Matching for Data Distribution Management on Shared-Memory Multiprocessors
In this paper we consider the problem of identifying intersections between
two sets of d-dimensional axis-parallel rectangles. This is a common problem
that arises in many agent-based simulation studies, and is of central
importance in the context of High Level Architecture (HLA), where it is at the
core of the Data Distribution Management (DDM) service. Several realizations of
the DDM service have been proposed; however, many of them are either
inefficient or inherently sequential. These are serious limitations since
multicore processors are now ubiquitous, and DDM algorithms -- being
CPU-intensive -- could benefit from additional computing power. We propose a
parallel version of the Sort-Based Matching algorithm for shared-memory
multiprocessors. Sort-Based Matching is one of the most efficient serial
algorithms for the DDM problem, but is quite difficult to parallelize due to
data dependencies. We describe the algorithm and compute its asymptotic running
time; we complete the analysis by assessing its performance and scalability
through extensive experiments on two commodity multicore systems based on a
dual socket Intel Xeon processor, and a single socket Intel Core i7 processor.Comment: Proceedings of the 21-th ACM/IEEE International Symposium on
Distributed Simulation and Real Time Applications (DS-RT 2017). Best Paper
Award @DS-RT 201
Data-efficient learning of feedback policies from image pixels using deep dynamical models
Data-efficient reinforcement learning (RL) in continuous state-action spaces using very high-dimensional observations remains a key challenge in developing fully autonomous systems. We consider a particularly important instance of this challenge, the pixels-to-torques problem, where an RL agent learns a closed-loop control policy ( torques ) from pixel information only. We introduce a data-efficient, model-based reinforcement learning algorithm that learns such a closed-loop policy directly from pixel information. The key ingredient is a deep dynamical model for learning a low-dimensional feature embedding of images jointly with a predictive model in this low-dimensional feature space. Joint learning is crucial for long-term predictions, which lie at the core of the adaptive nonlinear model predictive control strategy that we use for closed-loop control. Compared to state-of-the-art RL methods for continuous states and actions, our approach learns quickly, scales to high-dimensional state spaces, is lightweight and an important step toward fully autonomous end-to-end learning from pixels to torques
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A customizable multi-agent system for distributed data mining
We present a general Multi-Agent System framework for
distributed data mining based on a Peer-to-Peer model. Agent
protocols are implemented through message-based asynchronous
communication. The framework adopts a dynamic load balancing
policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances
Distributed data mining in grid computing environments
The official published version of this article can be found at the link below.The computing-intensive data mining for inherently Internet-wide distributed data, referred to as Distributed Data Mining (DDM), calls for the support of a powerful Grid with an effective scheduling framework. DDM often shares the computing paradigm of local processing and global synthesizing. It involves every phase of Data Mining (DM) processes, which makes the workflow of DDM very complex and can be modelled only by a Directed Acyclic Graph (DAG) with multiple data entries. Motivated by the need for a practical solution of the Grid scheduling problem for the DDM workflow, this paper proposes a novel two-phase scheduling framework, including External Scheduling and Internal Scheduling, on a two-level Grid architecture (InterGrid, IntraGrid). Currently a DM IntraGrid, named DMGCE (Data Mining Grid Computing Environment), has been developed with a dynamic scheduling framework for competitive DAGs in a heterogeneous computing environment. This system is implemented in an established Multi-Agent System (MAS) environment, in which the reuse of existing DM algorithms is achieved by encapsulating them into agents. Practical classification problems from oil well logging analysis are used to measure the system performance. The detailed experiment procedure and result analysis are also discussed in this paper
Modelling dynamic decision making with the ACT-R cognitive architecture
This paper describes a model of dynamic decision making in the Dynamic Stocks and Flows (DSF) task, developed using the ACT-R cognitive architecture. This task is a simple simulation of a water tank in which the water level must be kept constant whilst the inflow and outflow changes at varying rates. The basic functions of the model are based around three steps. Firstly, the model predicts the water level in the next cycle by adding the current water level to the predicted net inflow of water. Secondly, based on this projection, the net outflow of the water is adjusted to bring the water level back to the target. Thirdly, the predicted net inflow of water is adjusted to improve its accuracy in the future. If the prediction has overestimated net inflow then it is reduced, if it has underestimated net inflow it is increased. The model was entered into a model comparison competition-the Dynamic Stocks and Flows Challenge-to model human performance on four conditions of the DSF task and then subject the model to testing on five unseen transfer conditions. The model reproduced the main features of the development data reasonably well but did not reproduce human performance well under the transfer conditions. This suggests that the principles underlying human performance across the different conditions differ considerably despite their apparent similarity. Further lessons for the future development of our model and model comparison challenges are considered
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