http://hdl.handle.net/1721.1/5458 Search the Channel search http://dspace.mit.edu/simple-search http://hdl.handle.net/1721.1/41530 Title: Ignorable Information in Multi-Agent Scenarios <br/> <br/>Authors: Milch, Brian; Koller, Daphne <br/> <br/>Abstract: In some multi-agent scenarios, identifying observations that an agent can safely ignore reduces exponentially the size of the agent's strategy space and hence the time required to find a Nash equilibrium. We consider games represented using the multi-agent influence diagram (MAID) framework of Koller and Milch [2001], and analyze the extent to which information edges can be eliminated. We define a notion of a safe edge removal transformation, where all equilibria in the reduced model are also equilibria in the original model. We show that existing edge removal algorithms for influence diagrams are safe, but limited, in that they do not detect certain cases where edges can be removed safely. We describe an algorithm that produces the "minimal" safe reduction, which removes as many edges as possible while still preserving safety. Finally, we note that both the existing edge removal algorithms and our new one can eliminate equilibria where agents coordinate their actions by conditioning on irrelevant information. Surprisingly, in some games these "lost" equilibria can be preferred by all agents in the game. Sun, 11 May 2008 22:58:59 GMT http://hdl.handle.net/1721.1/41527 Title: Perfect Implementation of Normal-Form Mechanisms <br/> <br/>Authors: Izmalkov, Sergei; Lepinski, Matt; Micali, Silvio <br/> <br/>Abstract: Privacy and trust affect our strategic thinking, yet they have not been precisely modeled in mechanism design. In settings of incomplete information, traditional implementations of a normal-form mechanism ---by disregarding the players' privacy, or assuming trust in a mediator--- may not be realistic and fail to reach the mechanism's objectives. We thus investigate implementations of a new type.We put forward the notion of a perfect implementation of a normal-form mechanism M: in essence, an extensive-form mechanism exactly preserving all strategic properties of M, WITHOUT relying on a trusted mediator or violating the privacy of the players. We prove that ANY normal-form mechanism can be perfectly implemented by a PUBLIC mediator using envelopes and an envelope-randomizing device (i.e., the same tools used for running fair lotteries or tallying secret votes). Differently from a trusted mediator, a public one only performs prescribed public actions, so that everyone can verify that he is acting properly, and never learns any information that should remain private. Mon, 26 Feb 2007 22:58:59 GMT http://hdl.handle.net/1721.1/41526 Title: Gesture in Automatic Discourse Processing <br/> <br/>Authors: Eisenstein, Jacob <br/> <br/>Abstract: Computers cannot fully understand spoken language without access to the wide range of modalities that accompany speech. This thesis addresses the particularly expressive modality of hand gesture, and focuses on building structured statistical models at the intersection of speech, vision, and meaning.My approach is distinguished in two key respects. First, gestural patterns are leveraged to discover parallel structures in the meaning of the associated speech. This differs from prior work that attempted to interpret individual gestures directly, an approach that was prone to a lack of generality across speakers. Second, I present novel, structured statistical models for multimodal language processing, which enable learning about gesture in its linguistic context, rather than in the abstract.These ideas find successful application in a variety of language processing tasks: resolving ambiguous noun phrases, segmenting speech into topics, and producing keyframe summaries of spoken language. In all three cases, the addition of gestural features -- extracted automatically from video -- yields significantly improved performance over a state-of-the-art text-only alternative. This marks the first demonstration that hand gesture improves automatic discourse processing. Tue, 06 May 2008 22:58:59 GMT http://hdl.handle.net/1721.1/41519 Title: Efficient Object Recognition and Image Retrieval for Large-Scale Applications <br/> <br/>Authors: Lee, John J. <br/> <br/>Abstract: Algorithms for recognition and retrieval tasks generally call for both speed and accuracy. When scaling up to very large applications, however, we encounter additional significant requirements: adaptability and scalability. In many real-world systems, large numbers of images are constantly added to the database, requiring the algorithm to quickly tune itself to recent trends so it can serve queries more effectively. Moreover, the systems need to be able to meet the demands of simultaneous queries from many users. In this thesis, I describe two new algorithms intended to meet these requirements and give an extensive experimental evaluation for both. The first algorithm constructs an adaptive vocabulary forest, which is an efficient image-database model that grows and shrinks as needed while adapting its structure to tune itself to recent trends. The second algorithm is a method for efficiently performing classification tasks by comparing query images to only afixed number of training examples, regardless of the size of the image database. These two methods can be combined to create a fast, adaptable, and scalable vision system suitable for large-scale applications. I also introduce LIBPMK, a fast implementation of common computer vision processing pipelines such as that of the pyramid match kernel. This implementation was used to build several successful interactive applications as well as batch experiments for research settings. This implementation, in addition to the two new algorithms introduced by this thesis, are a step toward meeting the speed, adaptability, and scalability requirements of practical large-scale vision systems. Mon, 05 May 2008 22:58:59 GMT