This paper describes a novel algorithm called CON-MODP for computing Pareto optimal policies for deterministic multi-objective sequential decision problems. CON-MODP is a value iteration based multi-objective dynamic ...
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This paper describes a novel algorithm called CON-MODP for computing Pareto optimal policies for deterministic multi-objective sequential decision problems. CON-MODP is a value iteration based multi-objective dynamic programming algorithm that only computes stationary policies. We observe that for guaranteeing convergence to the unique Pareto optimal set of deterministic stationary policies, the algorithm needs to perform a policy evaluation step on particular policies that are inconsistent in a single state that is being expanded. We prove that the algorithm converges to the Pareto optimal set of value functions and policies for deterministic infinite horizon discounted multi-objective Markov decision processes. Experiments show that CON-MODP is much faster than previous multi-objective value iteration algorithms.
This paper describes an extension to the component-based programming model to support real-time dynamic guarantee for distributed applications. The extended model aims to include an acceptance tests to component-based...
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This paper describes an extension to the component-based programming model to support real-time dynamic guarantee for distributed applications. The extended model aims to include an acceptance tests to component-based servers at bind time. We present the mapping of our model to the CORBA Component Model, the architecture to support the dynamic guarantee in component-based middleware, the implementation of this architecture in CIAO (Component Integrated ACE ORB) and the result of experiments run to evaluate the cost of the mechanisms used
Fault tolerance is a desirable feature in distributed high-performance systems, since applications tend to run for long periods of time and faults become more likely as the number of nodes in the system increase. Howe...
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Fault tolerance is a desirable feature in distributed high-performance systems, since applications tend to run for long periods of time and faults become more likely as the number of nodes in the system increase. However, most distributed environments lack any fault tolerant features, since they tend to be hard to implement and use, and often hurt performance dramatically. In this paper we discuss how we successfully added fault-tolerance to the Anthill distributed programming environment by using an application-level checkpoint/rollback solution. The programming model offers an abstraction where the programmer can easily identify points during the execution where the communication pattern is well defined, forming a consistent cut where checkpoints may be saved consistently without requiring extra communication, avoiding any domino effect during recovery from faults. We present the new abstractions for fault tolerance, describe how the solution was implemented and present performance results that show the efficiency of the solution with both regular and irregular applications.
In this paper, we briefly describe existing principles and stages for generating code from OCL expressions pointing out the drawbacks that cause inefficiencies of the resulting code. The proposed improvement of the tr...
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In this paper, we briefly describe existing principles and stages for generating code from OCL expressions pointing out the drawbacks that cause inefficiencies of the resulting code. The proposed improvement of the transformation is based on extended abstract syntax trees (AST) with context-specific attributes. Principles for defining such attributes on AST trees and an example of transformation is presented.
In this paper we introduce a new model-based approach for a data-efficient modelling and control of reinforcement learning problems in discrete time. Our architecture is based on a recurrent neural network (RNN) with ...
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In this paper we introduce a new model-based approach for a data-efficient modelling and control of reinforcement learning problems in discrete time. Our architecture is based on a recurrent neural network (RNN) with dynamically consistent overshooting, which we extend by an additional control network. The latter has the particular task to learn the optimal policy. This approach has the advantage that by using a neural network we can easily deal with high-dimensions and consequently are able to break Bellman's curse of dimensionality. Further due to the high system-identification quality of RNN our method is highly data-efficient. Because of its properties we refer to our new model as recurrent control neural network (RCNN). The network is tested on a standard reinforcement learning problem, namely the cart-pole balancing, where it shows especially in terms of data-efficiency outstanding results
This paper proposes a , the "ambit' of an action, that allows the degree of distribution of an action in a multiagent system to be quantified without regard to its functionality. It demonstrates the use of th...
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This paper proposes a , the "ambit' of an action, that allows the degree of distribution of an action in a multiagent system to be quantified without regard to its functionality. It demonstrates the use of that notion in the design, analysis and implementation of dynamically-reconfigurable multi-agent systems. It distinguishes between the extensional (or system) view and intensional (or agent-based) view of such a system and shows how, using the notion of ambit, the step-wise derivation paradigm of formal methods can be used to derive the latter from the former. In closing it addresses the manner in which these ideas inform studies in the ethics of systems of artificial agents.
Despite its widespread use, concurrent programming is still plagued by reliability problems, such as race conditions and deadlock, not found in sequential programs. We present a concurrency framework to help developer...
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Despite its widespread use, concurrent programming is still plagued by reliability problems, such as race conditions and deadlock, not found in sequential programs. We present a concurrency framework to help developers avoid these error conditions, and make it possible to verify their absence through static analysis.
Transactional memory (TM) provides mechanisms that promise to simplify parallel programming by eliminating the need for locks and their associated problems (deadlock, livelock, priority inversion, convoying). For TM t...
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Transactional memory (TM) provides mechanisms that promise to simplify parallel programming by eliminating the need for locks and their associated problems (deadlock, livelock, priority inversion, convoying). For TM to be adopted in the long term, not only does it need to deliver on these promises, but it needs to scale to a high number of processors. To date, proposals for scalable TM have relegated livelock issues to user-level contention managers. This paper presents the first scalable TM implementation for directory-based distributed shared memory systems that is livelock free without the need for user-level intervention. The design is a scalable implementation of optimistic concurrency control that supports parallel commits with a two-phase commit protocol, uses write-back caches, and filters coherence messages. The scalable design is based on transactional coherence and consistency (TCC), which supports continuous transactions and fault isolation. A performance evaluation of the design using both scientific and enterprise benchmarks demonstrates that the directory-based TCC design scales efficiently for NUMA systems up to 64 processors
This paper describes the development and testing of control of the OmniTread OT-4 robot by the seventh generation (7G) control system. Control of OT-4 was developed in the Yobotics 3D simulator by an iterative process...
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This paper describes the development and testing of control of the OmniTread OT-4 robot by the seventh generation (7G) control system. Control of OT-4 was developed in the Yobotics 3D simulator by an iterative process combining genetic algorithm, learning and analytic programming techniques. The control system developed in simulation was tested by controlling the real OT-4 robot in the laboratory. The performance of the real OT-4 robot under 7G control on stairs, parallel bars, a slalom course, and stairs with obstacles corresponded well to the simulated performance on which development of the control system was based.
There are two popular parallel programming paradigms available to high performance computing users such as engineering and physics professionals: message passing and distributed shared memory. It is interesting to hav...
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There are two popular parallel programming paradigms available to high performance computing users such as engineering and physics professionals: message passing and distributed shared memory. It is interesting to have a comparative evaluation of these paradigms to choose the most adequate one. In this work, we present a performance comparison of these two programming paradigms using a computational physics problem as a case study. The self-gravitating ring model (Hamiltonian mean field model) for N particles is extensively studied in the literature as a simplified model for long range interacting systems in Physics. We parallelized and evaluated the performance of a simulation that uses the symplectic integrator to model an N particle system. From the obtained results it is possible to observe that message passing implementation of the symplectic integrator presents better results than distributed shared memory implementation.
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