http://smartech.gatech.edu/handle/1853/14327 Computing’s interaction with users and their environments Search the Channel search http://smartech.gatech.edu/simple-search http://smartech.gatech.edu/handle/1853/19893 Title: Horizon-based Value Iteration <br/> <br/>Authors: Zang, Peng; Irani, Arya; Isbell, Charles <br/> <br/>Abstract: We present a horizon-based value iteration algorithm called Reverse Value Iteration (RVI). Empirical results on a variety of domains, both synthetic and real, show RVI often yields speedups of several orders of magnitude. RVI does this by ordering backups by horizons, with preference given to closer horizons, thereby avoiding many unnecessary and incorrect backups. We also compare to related work, including prioritized and partitioned value iteration approaches, and show that our technique performs favorably. The techniques presented in RVI are complementary and can be used in conjunction with previous techniques. We prove that RVI converges and often has better (but never worse) complexity than standard value iteration. To the authors’ knowledge, this is the first comprehensive theoretical and empirical treatment of such an approach to value iteration. Sun, 29 Oct 2006 22:58:59 GMT http://smartech.gatech.edu/handle/1853/19892 Title: Statically Stable Assembly Sequence Generation for Many Identical Blocks <br/> <br/>Authors: Wolff, Sebastien Jean; Ebert-Uphoff, Imme; Lipkin, Harvey <br/> <br/>Abstract: This work develops optimal assembly sequences for modular building blocks. The underlying concept is that an automated device could take a virtual shape such as a CAD file, and decide how to physically build the shape using simple, identical building blocks. The primary focus of this work is the development of methods for generating assembly sequences in a time-feasible manner that ensure static stability at each step of the assembly. This is accomplished by a multi-hierarchical rule-based approach, consisting of a set of low-level, mid-level and high-level assembly rules. Both high-level and mid-level assembly rules are primarily based on static considerations. The best performing rules are presented and their behavior is analyzed. Mon, 15 Oct 2007 22:58:59 GMT http://smartech.gatech.edu/handle/1853/19891 Title: Remixing Authorship: Reconfiguring the Author in Online Video Remix Culture <br/> <br/>Authors: Diakopoulos, Nicholas; Luther, Kurt; Medynskiy, Yevgeniy (Eugene); Essa, Irfan <br/> <br/>Abstract: In an abstract sense, authorship entails the constrained selection or generation of media and the organization and layout of that media in a larger structure. But authorship is more than just selection and organization; it is a complex construct incorporating concepts of originality, authority, intertextuality, and attribution. In this paper we explore these concepts as they relate to authorship and ask how they are changing in light of modes of collaborative authorship in remix culture. A detailed qualitative study of an online video remixing site is presented to help understand how the constraints of that environment are impacting authorial constructs. We discuss users’ self-conceptions as authors, and how values related to authorship are reflected to users through the interface and design of the site’s remixing and community tools. Finally, we present some implications of this work for the design of online communities for collaborative media creation and remixing. Sun, 29 Oct 2006 22:58:59 GMT http://smartech.gatech.edu/handle/1853/19887 Title: Anticipatory Robot Control for a Partially Observable Environment Using Episodic Memories <br/> <br/>Authors: Endo, Yoichiro <br/> <br/>Abstract: This paper explains an episodic-memory based approach for computing anticipatory robot behavior in a partially observable environment. Inspired by biological findings on the mammalian hippocampus, here, the episodic memories retain a sequence of experienced observation, behavior, and reward. Incorporating multiple machine learning methods, this approach attempts to help reducing the computational burden of the partially observable Markov decision process (POMDP). In particular, the proposed computational reduction techniques include: 1) abstraction of the state space via temporal difference learning; 2) abstraction of the action space by utilizing motor schemata; 3) narrowing down the state space in terms of the goals by employing instance-based learning; 4) elimination of the value-iteration by assuming a unidirectional-linear-chaining formation of the state space; 5) reduction of the state-estimate computation by exploiting the property of the Poisson distribution; and 6) trimming the history length by imposing the cap on the number of episodes that are computed. Furthermore, claims 5) and 6) were empirically verified, and it was confirmed that the state estimation can be in fact computed in an O(n) time (where n is the number of the states), more efficient than a conventional Kalman-filter based approach of O(n2). Sun, 29 Oct 2006 22:58:59 GMT