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ACM Transactions on Autonomous and Adaptive Systems (TAAS), Volume 8 Issue 1, April 2013

Editorial
Manish Parashar, Franco Zambonelli
Article No.: 1
DOI: 10.1145/2451248.2451249

Robust convention emergence in social networks through self-reinforcing structures dissolution
Daniel Villatoro, Jordi Sabater-Mir, Sandip Sen
Article No.: 2
DOI: 10.1145/2451248.2451250

Convention emergence solves the problem of choosing, in a decentralized way and among all equally beneficial conventions, the same convention for the entire population in the system for their own benefit. Our previous work has shown that reaching...

Convergence results for ant routing algorithms via stochastic approximation
Punyaslok Purkayastha, John S. Baras
Article No.: 3
DOI: 10.1145/2451248.2451251

In this article, we provide convergence results for an Ant-based Routing Algorithm (ARA) for wireline, packet-switched communication networks, that are acyclic. Such algorithms are inspired by the foraging behavior of ants in nature. We consider...

Adapting scientific workflow structures using multi-objective optimization strategies
Irfan Habib, Ashiq Anjum, Richard Mcclatchey, Omer Rana
Article No.: 4
DOI: 10.1145/2451248.2451252

Scientific workflows have become the primary mechanism for conducting analyses on distributed computing infrastructures such as grids and clouds. In recent years, the focus of optimization within scientific workflows has primarily been on...

Learning user preferences for adaptive pervasive environments: An incremental and temporal approach
Sarah Gallacher, Eliza Papadopoulou, Nick K. Taylor, M. Howard Williams
Article No.: 5
DOI: 10.1145/2451248.2451253

Personalization mechanisms often employ behavior monitoring and machine learning techniques to aid the user in the creation and management of a preference set that is used to drive the adaptation of environments and resources in line with...

A state-dependent time evolving multi-constraint routing algorithm
Abdelhamid Mellouk, Said Hoceini, Sherali Zeadally
Article No.: 6
DOI: 10.1145/2451248.2451254

This article proposes a state-dependent routing algorithm based on a global optimization cost function whose parameters are learned from the real-time state of the network with no a priori model. The proposed approach samples, estimates, and...