2813554
Trading Agents Competing: Performance, Progress, and Market Effectiveness
The annual trading agent competition offers agent designers a forum for evaluating programmed trading techniques in a challenging market scenario. TAC aims to spur research by enabling researchers to compare techniques on a common problem and build on each other's ideas. A fixed set of assumptions and environment settings facilitates communication of methods and results. As a multiyear event, TAC lets researchers observe trading agents' progress over time, in effect accelerating the evolution of an adapted population of traders. Given all the participant effort invested, it is incumbent on us to learn as much from the experience as possible. After three years of TAC, we're ready to examine there we stand. To do this, we used data from actual TAC tournaments and some post-competition experimentation. We based our analysis almost entirely on outcomes (profits and allocations), with very little direct accounting for specific agent techniques.
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.
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.
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
15084916
Computing the channel capacity and rate-distortion function with two-sided state information
In this correspondence, we present iterative algorithms that numerically compute the capacity-power and rate-distortion functions for coding with two-sided state information. Numerical examples are provided to demonstrate efficiency of our algorithms.
29681392
Schedulability-oriented replication of periodic tasks in distributed real-time systems
We consider the schedulability-oriented replication problem of a set of periodic real-time tasks where each task can be decomposed into several modules and intermodule communications. The objective is to find an allocation in which there exists a feasible schedule for the given task set. In this paper, we adopt a communication model where the replication of modules is not for the sake of fault tolerance but for increasing the degree of schedulability. To solve the problem, we develop a replication technique and embed the technique in a simulated annealing algorithm. Experimental results show that such replication may lead to a higher degree of schedulability and obtain a feasible solution.
2623430
Manifold reconstruction from point samples
We present an algorithm to "reconstruct" a smooth <i>k</i>-dimensional manifold <i>M</i> embedded in an Euclidean space R<sup>d</sup> from a "sufficiently dense" point sample from the manifold. The algorithm outputs a simplicial manifold that is homeomorphic and geometrically close to <i>M.</i> The running time is <i>O</i>(<i>n</i> log <i>n</i>) where <i>n</i> is the number of points in the sample (the multiplicative constant depends exponentially on the dimension though).
15323037
Successive refinement for the Wyner-Ziv problem and layered code design
We examine successive refinement for the Wyner-Ziv problem described in a recent paper by Steinberg and Merhav, where the authors showed that if the side information for all stages is identical, then the jointly Gaussian source with squared error distortion measure is successively refinable. We first extend this result to the case where the difference between the source and the side information is Gaussian and independent of the side information. As a byproduct, we give an alternative proof that the Wyner-Ziv problem for these sources has no rate loss-a result that was recently shown by Pradhan et al. through invoking the duality between the Gaussian Wyner-Ziv problem and the Costa problem. We then perform layered Wyner-Ziv code design for this general type of source based on nested scalar quantization, bit-plane coding, and low-density parity check (LDPC) code-based Slepian-Wolf coding (source coding with side information). We show that density evolution can be used to analyze the Slepian-Wolf code performance, provided that certain symmetry conditions, which have been dubbed dual symmetry, are satisfied by the hypothetical channel between the source and the side information. We also show that the dual symmetry condition is indeed satisfied by the hypothetical channel in our Slepian-Wolf coding setup. This justifies the use of density evolution in our LDPC code-based Slepian-Wolf code design for Wyner-Ziv coding. For the jointly Gaussian source, our layered coder performs 1.29 to 3.45 dB from the Wyner-Ziv bound for rates ranging from 0.47 to 4.68 bits per sample. When the side information is Laplacian and the source equals the side information plus an independent Gaussian noise term, our layered coder performs 1.33 to 3.90 dB from the Wyner-Ziv bound for rates ranging from 0.48 to 4.64 bits per sample.
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.