1223930
Approximate Strategic Reasoning through Hierarchical Reduction of Large Symmetric Games
To deal with exponential growth in the size of a game with the number of agents, we propose an approximation based on a hierarchy of reduced games. The reduced game achieves savings by restricting the number of agents playing any strategy to fixed multiples. We validate the idea through experiments on randomly generated local-effect games. An extended application to strategic reasoning about a complex trading scenario motivates the approach, and demonstrates methods for game-theoretic reasoning over incompletely-specified games at multiple levels of granularity. Motivation Consider the task of selecting among a large set of strategies to play in an 8-player game. Through careful judgment you manage to narrow down the candidates to a reasonable number of strategies (say 35). Because the performance of a strategy for one agent depends on the strategies of the other seven, you wish to undertake a game-theoretic analysis of the situation. Determining the payoff for a particular strategy profile is expensive, however, as your observations of prior game instances are quite limited, and the only operational description of the game is in the form of a simulator that takes a non-negligible time (say 10 minutes) to produce one outcome. Moreover, since the environment is stochastic, numerous samples (say 12) are required to produce a reliable estimate for even one profile. At two hours per profile, ex
7697772
Walverine: a Walrasian trading agent
TAC-02 was the third in a series of Trading Agent Competition events fostering research in automating trading strategies by showcasing alternate approaches in an open-invitation market game. TAC presents a challenging travel-shopping scenario where agents must satisfy client preferences for complementary and substitutable goods by interacting through a variety of market types. Michigan's entry, Walverine, attempts to bid optimally based on a competitive analysis of the TAC travel economy. Walverine's approach embodies several techniques not previously employed in TAC: (1) price prediction based on competitive equilibrium analysis, (2) hedged optimization with respect to a model of outlier prices, (3) optimal bidding based on a decision-theoretic calculation of bid actions, and (4) reinforcement learning for CDA trading strategies. Each of these is potentially applicable in a broad class of trading environments.
2640918
Iterated Weaker-than-Weak Dominance
We introduce a weakening of standard game-theoretic dominance conditions, called δ-dominance, which enables more aggressive pruning of candidate strategies at the cost of solution accuracy. Equilibria of a game obtained by eliminating a δ-dominated strategy are guaranteed to be approximate equilibria of the original game, with degree of approximation bounded by the dominance parameter, δ. We can apply elimination of δ-dominated strategies iteratively, but the δ for which a strategy may be eliminated depends on prior eliminations. We discuss implications of this order independence, and propose greedy heuristics for determining a sequence of eliminations to reduce the game as far as possible while keeping down costs. A case study analysis of an empirical 2-player game serves to illustrate the technique, and demonstrate the utility of weaker-than-weak dominance pruning.
751641
Market-Based Resource Allocation for Information-Collection Scenarios
Dynamic decentralized resource allocation for information-collection can play a critical role in emergency scenarios. We explore two simplified scenarios for information-collection, and define market games for allocating resources to interdependent tasks. Experiments with a market game for disaster-response illustrate our general methodology for analyzing strategic interactions in such an environment.
11053365
Making Self-Adaptation an Engineering Reality
In this paper, we envision a world where a software engineer could take an existing software system, specify, for a set of properties of interest, an objective, conditions for change, and strategies for their adaptation and, within a few man weeks, make that system self-adaptive where it was not before. We describe how our approach generalizes to different classes of systems and holds promise for cost-effective, dynamic system self-adaptation to become an engineering reality.
17584518
On beta-skeleton as a subgraph of the minimum weight triangulation
Abstract Given a set S of n points in the plane, a triangulation is a maximal set of non-intersecting edges connecting the points in S . The weight of the triangulation is the sum of the lengths of the edges. In this paper, we show that for β>1/ sin κ , the β -skeleton of S is a subgraph of a minimum weight triangulation of S , where κ= tan −1 (3/ 2 3 )≈ π /3.1 . There exists a four-point example such that the β -skeleton for β sin ( π /3) is not a subgraph of the minimum weight triangulation.
2936245
On failure recoverability of client-server applications in mobile wireless environments
Analytical results for the Cdf of the failure recovery time for client-server applications in mobile wireless environments characterized by logging, and mobility handoff strategies for facilitating failure recovery are reported in the paper. The results can be applied to determine if a mobile application can satisfy its recoverability requirement upon a mobile host failure when operating under a set of parameter values characterizing the mobile application, the underlying client-server environment, and the logging & mobility handoff strategies adopted by the mobile application. Model parameters which affect the shape of the failure recovery time Cdf for two mobility handoff strategies, namely, Eager and Lazy, are identified, and their effects are analyzed, with numerical data and result interpretations given. A tradeoff analysis between the cost invested by these two mobility handoff strategies for maintaining the logging and checkpoint information before failure versus the return of investment in terms of improved failure recoverability is given, and the best checkpoint interval period that would yield the best return of investment for the eager mobility handoff strategy over the lazy strategy is identified.
15245491
Efficient Expected-Case Algorithms for Planar Point Location
Planar point location is among the most fundamental search problems in computational geometry. Although this problem has been heavily studied from the perspective of worst-case query time, there has been surprisingly little theoretical work on expected-case query time. 
 
We are given an n-vertex planar polygonal subdivision S satisfying some weak assumptions (satisfied, for example, by all convex subdivisions). 
 
We are to preprocess this into a data structure so that queries can be answered efficiently. We assume that the two coordinates of each query point are generated independently by a probability distribution also satisfying some weak assumptions (satisfied, for example, by the uniform distribution). 
 
In the decision tree model of computation, it is well-known from information theory that a lower bound on the expected number of comparisons is entropy(S). We provide two data structures, one of size O(n2) that can answer queries in 2 entropy(S) + O(1) expected number of comparisons, and another of size O(n) that can answer queries in (4 + O(1/√log n)) entropy(S)+O(1) expected number of comparisons. These structures can be built in O(n2) and O(n log n) time respectively. Our results are based on a recent result due to Arya and Fu, which bounds the entropy of overlaid subdivisions.
9588683
Performance evaluation of an admission control algorithm: dynamic threshold with negotiation
An admission control algorithm for a multimedia server is responsible for determining if a new request can be accepted without violating the QoS requirements of the existing requests in the system. Most admission control algorithms treat every request uniformly and hence optimize the system performance by maximizing the number of admitted and served requests. In practice, requests might have different levels of importance to the system. Requests offering high contribution or reward to the system performance deserve priority treatment. Failure of accepting a high-priority request would incur high penalty to the system.A novel threshold-based admission control algorithm with negotiation for two priority classes of requests is proposed in our previous study. The server capacity is divided into three partitions based on the threshold values: one for each class of requests and one common pool shared by two classes of requests. Reward and penalty are adopted in the proposed system model. High-priority requests are associated with higher values of reward as well as penalty than low-priority ones. In this paper, given the characteristics of the system workload, the proposed analytical models aim to finds the best partitions, optimizing the system performance based on the objective function of the total reward minus the total penalty. The negotiation mechanism reduces the QoS requirements of several low-priority clients, by cutting out a small fraction of the assigned server capacity, to accept a new high-priority client and to achieve a higher net earning value. Stochastic Petri-Net model is used to find the optimal threshold values and two analytical methods are developed to find sub-optimal settings. The experiment results show that the sub-optimal solutions found by the proposed analytical methods are very close to optimal ones. The methods enable the algorithm to dynamically adjust the threshold values, based on the characteristics of the system workload, to achieve higher system performance.