Loizos Michael

ICE: an iterative combinatorial exchange

We present the first design for a fully expressive iterative combinatorial exchange (ICE). The exchange incorporates a tree-based bidding language that is concise and expressive for CEs. Bidders specify lower and upper bounds on their value for different trades. These bounds allow price discovery and useful preference elicitation in early rounds, and allow termination with an efficient trade despite partial information on bidder valuations. All computation in the exchange is carefully optimized to exploit the structure of the bid-trees and to avoid enumerating trades. A proxied interpretation of a revealed-preference activity rule ensures progress across rounds. A VCG-based payment scheme that has been shown to mitigate opportunities for bargaining and strategic behavior is used to determine final payments. The exchange is fully implemented and in a validation phase.

Modular-ε: an elaboration tolerant approach to the ramification and qualification problems

We describe MediaObjects/InlineFigure2.png (MediaObjects/InlineFigure3.png), a specialized, model-theoretic logic for narrative reasoning about actions, able to represent non-deterministic domains involving concurrency, static laws (constraints) and indirect effects (ramifications). We give formal results which characterize MediaObjects/InlineFigure4.pnghigh degree of modularity and elaboration tolerance, and show how these properties help to separate out, and provide a principled solutions to, the endogenous and exogenous qualification problems. We also show how a notion of (micro) processes can be used to facilitate reasoning at the dual levels of temporal granularity necessary for narrative-based domains involving “instantaneous” series of indirect and knock-on effects.

Reasoning about actions and change in Answer Set Programming

This paper studies computational issues related to the problem of reasoning about actions and change (RAC) by exploiting its link with the Answer Set Programming paradigm. It investigates how increasing the expressiveness of a RAC formalism so that it can capture the three major problems of frame, ramification and qualification, affects its computational complexity, and how a solution to these problems can be implemented within Answer Set Programming. Our study is carried out within the particular language e. It establishes a link between e and Answer Set Programming by presenting encodings of different versions of this language into logic programs under the answer set semantics. This provides a computational realization of solutions to problems related to reasoning about actions and change, that can make use of the recent development of effective systems for Answer Set Programming.

The price of defense

We consider a strategic game with two classes of confronting randomized players on a graph G(V, E): νattackers, each choosing vertices and wishing to minimize the probability of being caught, and a defender, who chooses edges and gains the expected number of attackers it catches. The Price of Defense is the worst-case ratio, over all Nash equilibria, of the optimal gain of the defender over its gain at a Nash equilibrium. We provide a comprehensive collection of trade-offs between the Price of Defense and the computational efficiency of Nash equilibria. – Through reduction to a Two-Players, Constant-Sum Game, we prove that a Nash equilibrium can be computed in polynomial time. The reduction does not provide any apparent guarantees on the Price of Defense. – To obtain such, we analyze several structured Nash equilibria: – In a Matching Nash equilibrium, the support of the defender is an Edge Cover. We prove that they can be computed in polynomial time, and they incur a Price of Defense of α(G), the Independence Number of G. – In a Perfect Matching Nash equilibrium, the support of the defender is a Perfect Matching. We prove that they can be computed in polynomial time, and they incur a Price of Defense of

Causal learnability

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Evolvability via the Fourier transform

. – In a Defender Uniform Nash equilibrium, the defender chooses uniformly each edge in its support. We prove that they incur a Price of Defense falling between those for Matching and Perfect Matching Nash Equilibria; however, it is

Congestion Control in Wireless Sensor Networks Based on the Bird Flocking Behavior

{\cal NP}

Applying swarm intelligence to a novel congestion control approach for wireless sensor networks

-complete to decide their existence. – In an Attacker Symmetric and Uniform Nash equilibrium, all attackers have a common support on which each uses a uniform distribution. We prove that they can be computed in polynomial time and incur a Price of Defense of either

Knowledge Qualification through Argumentation

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Specifying and monitoring market mechanisms using rights and obligations

or α(G).