@conference {18922,
	title = {The Life Game: Cognitive Strategies for Repeated Stochastic Games},
	year = {2011},
	month = {2011/10//},
	pages = {95 - 102},
	abstract = {Standard models in bio-evolutionary game theory involve repetitions of a single stage game (e.g., the Prisoner{\textquoteright}s Dilemma or the Stag Hunt); but it is clear that repeatedly playing the same stage game is not an accurate model of most individuals{\textquoteright} lives. Rather, individuals{\textquoteright} interactions with others correspond to many different kinds of stage games. In this work, we concentrate on discovering behavioral strategies that are successful for the life game, in which the stage game is chosen stochastically at each iteration. We present a cognitive agent model based on Social Value Orientation (SVO) theory. We provide extensive evaluations of our model{\textquoteright}s performance, both against standard agents from the game theory literature and against a large set of life-game agents written by students in two different countries. Our empirical results suggest that for life-game strategies to be successful in environments with such agents, it is important (i) to be unforgiving with respect to trust behavior and (ii) to use adaptive, fine-grained opponent models of the other agents.},
	keywords = {adaptive model, behavioral strategy, bioevolutionary game theory, cognitive agent model, fine-grained opponent model, game theory, iteration, iterative methods, life game, prisoner{\textquoteright}s dilemma, repeated stochastic games, social value orientation theory, stag hunt, stage game, Stochastic processes, trust behavior},
	doi = {10.1109/PASSAT/SocialCom.2011.62},
	author = {Cheng,Kan-Leung and Zuckerman,I. and Nau, Dana S. and Golbeck,J.}
}
@conference {16470,
	title = {ApproxRank: Estimating Rank for a Subgraph},
	booktitle = {IEEE 25th International Conference on Data Engineering, 2009. ICDE {\textquoteright}09},
	year = {2009},
	month = {2009/04/29/March},
	pages = {54 - 65},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Customized semantic query answering, personalized search, focused crawlers and localized search engines frequently focus on ranking the pages contained within a subgraph of the global Web graph. The challenge for these applications is to compute PageRank-style scores efficiently on the subgraph, i.e., the ranking must reflect the global link structure of the Web graph but it must do so without paying the high overhead associated with a global computation. We propose a framework of an exact solution and an approximate solution for computing ranking on a subgraph. The IdealRank algorithm is an exact solution with the assumption that the scores of external pages are known. We prove that the IdealRank scores for pages in the subgraph converge. Since the PageRank-style scores of external pages may not typically be available, we propose the ApproxRank algorithm to estimate scores for the subgraph. Both IdealRank and ApproxRank represent the set of external pages with an external node L1 and extend the subgraph with links to L1. They also modify the PageRank-style transition matrix with respect to L1. We analyze the L1 distance between IdealRank scores and ApproxRank scores of the subgraph and show that it is within a constant factor of the L1 distance of the external pages (e.g., the true PageRank scores and uniform scores assumed by ApproxRank). We compare ApproxRank and a stochastic complementation approach (SC), a current best solution for this problem, on different types of subgraphs. ApproxRank has similar or superior performance to SC and typically improves on the runtime performance of SC by an order of magnitude or better. We demonstrate that ApproxRank provides a good approximation to PageRank for a variety of subgraphs.},
	keywords = {Application software, ApproxRank, Computer applications, Crawlers, customized semantic query answering, Data engineering, Educational institutions, Explosions, focused crawlers, global Web graph, graph theory, IdealRank algorithm, Internet, localized search engines, PageRank-style, personalized search, Query processing, Runtime, Search engines, Stochastic processes, Web pages},
	isbn = {978-1-4244-3422-0},
	doi = {10.1109/ICDE.2009.108},
	author = {Yao Wu and Raschid, Louiqa}
}
@conference {12014,
	title = {Integration of visual and inertial information for egomotion: a stochastic approach},
	booktitle = {Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006},
	year = {2006},
	month = {2006/05/15/19},
	pages = {2053 - 2059},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present a probabilistic framework for visual correspondence, inertial measurements and egomotion. First, we describe a simple method based on Gabor filters to produce correspondence probability distributions. Next, we generate a noise model for inertial measurements. Probability distributions over the motions are then computed directly from the correspondence distributions and the inertial measurements. We investigate combining the inertial and visual information for a single distribution over the motions. We find that with smaller amounts of correspondence information, fusion of the visual data with the inertial sensor results in much better egomotion estimation. This is essentially because inertial measurements decrease the "translation-rotation" ambiguity. However, when more correspondence information is used, this ambiguity is reduced to such a degree that the inertial measurements provide negligible improvement in accuracy. This suggests that inertial and visual information are more closely integrated in a compositional sense},
	keywords = {Computer vision, data mining, Distributed computing, egomotion estimation, Gabor filters, Gravity, inertial information, inertial sensor, Laboratories, Motion estimation, Noise measurement, Probability distribution, probability distributions, Rotation measurement, stochastic approach, Stochastic processes, visual information},
	isbn = {0-7803-9505-0},
	doi = {10.1109/ROBOT.2006.1642007},
	author = {Domke, J. and Aloimonos, J.}
}
@conference {11900,
	title = {A learning automata based power management for ad-hoc networks},
	booktitle = {2005 IEEE International Conference on Systems, Man and Cybernetics},
	volume = {4},
	year = {2005},
	month = {2005/10//},
	pages = {3569- 3573 Vol. 4 - 3569- 3573 Vol. 4},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Power management is a very important aspect of ad-hoc networks. It directly impacts the network throughput among other network metrics. On the other hand, transmission power management may result in disconnected networks and increased level of collisions. In this paper, we introduce a transmission power control based on stochastic learning automata (SLA) to modify the transmission power. Based on the level of successful transmissions and the level of packet retransmissions, the SLA will modify the transmission power level either by increasing it or decreasing it. The probabilistic nature of SLA makes it a useful choice for ad-hoc networks. Using the network simulator NS, we show that using SLA for transmission power will result in an increased system bandwidth and a decrease in the collision levels.},
	keywords = {ad hoc networks, ad-hoc networks, Computer network management, Computer networks, Energy management, Engineering management, learning automata, network metrics, network simulator, packet retransmissions, power control, power system management, power transmission control, stochastic learning automata, stochastic learning automta, Stochastic processes, system bandwidth, Technology management, Throughput, transmission power control, transmission power level, transmission power management},
	isbn = {0-7803-9298-1},
	doi = {10.1109/ICSMC.2005.1571701},
	author = {El-Osery,A. I and Baird,D. and Abd-Almageed, Wael}
}
@conference {11905,
	title = {Non-parametric expectation maximization: a learning automata approach},
	booktitle = {IEEE International Conference on Systems, Man and Cybernetics, 2003},
	volume = {3},
	year = {2003},
	month = {2003/10//},
	pages = {2996- 3001 vol.3 - 2996- 3001 vol.3},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The famous expectation maximization technique suffers two major drawbacks. First, the number of components has to be specified apriori. Also, the expectation maximization is sensitive to initialization. In this paper, we present a new stochastic technique for estimating the mixture parameters. Parzen Window is used to estimate a discrete estimate of the PDF of the given data. Stochastic learning automata is then used to select the mixture parameters that minimize the distance between the discrete estimate of the PDF and the estimate of the expectation maximization. The validity of the proposed approach is verified using bivariate simulation data.},
	keywords = {Automatic speech recognition, bivariate simulation data, Computational modeling, Computer vision, Density functional theory, expectation maximization technique, learning automata, mixture parameters estimation, nonparametric expectation maximization, nonparametric statistics, optimisation, parameter estimation, Parzen Window, PDF, probability, probability density function, Speech processing, Speech recognition, stochastic learning automata, Stochastic processes, stochastic technique},
	isbn = {0-7803-7952-7},
	doi = {10.1109/ICSMC.2003.1244347},
	author = {Abd-Almageed, Wael and El-Osery,A. and Smith,C.E.}
}
@conference {19501,
	title = {On-chip stochastic communication [SoC applications]},
	booktitle = {Design, Automation and Test in Europe Conference and Exhibition, 2003},
	year = {2003},
	month = {2003///},
	pages = {790 - 795},
	abstract = {As CMOS technology scales down into the deep-submicron (DSM) domain, the costs of design and verification for systems-on-chip (SoCs) are rapidly increasing due to the inefficiency of traditional CAD tools. Relaxing the requirement of 100\% correctness for devices and interconnects drastically reduces the costs of design but, at the same time, requires that SoCs be designed with some system-level fault-tolerance. In this paper, we introduce a new communication paradigm for SoCs, namely stochastic communication. The newly proposed scheme not only separates communication from computation, but also provides the required built-in fault-tolerance to DSM failures, is scalable and cheap to implement. For a generic tile-based architecture, we show how a ubiquitous multimedia application (an MP3 encoder) can be implemented using stochastic communication in an efficient and robust manner. More precisely, up to 70\% data upsets, 80\% packet drops because of buffer overflow, and severe levels of synchronization failures can be tolerated while maintaining a low latency.},
	keywords = {buffer overflow, CMOS technology, CMOS technology scaling, Costs, data upsets, Design automation, Digital audio players, Fault tolerance, Fault tolerant systems, generic tile-based architecture, integrated circuit design, integrated circuit interconnections, interconnect correctness requirements, Logic Design, logic simulation, MP3 encoder, Multiprocessor interconnection networks, on-chip fault-tolerance, on-chip stochastic communication, packet drops, Pervasive computing, Robustness, SoC design, SoC verification, Stochastic processes, Synchronisation, synchronization failures, system latency, system-level fault-tolerance, system-on-chip, systems-on-chip, video coding},
	author = {Tudor Dumitras and Marculescu, R.}
}
