30243464
Planar Graph Blocking for External Searching
We present a new scheme for storing a planar graph in external memory so that any online path can be traversed in an I-O efficient way. Our storage scheme significantly improves the previous results for planar graphs with bounded face size. We also prove an upper bound on I-O efficiency of any storage scheme for well-shaped triangulated meshes. For these meshes, our storage scheme achieves optimal performance.
1068367
An Efficient Output-Size Sensitive Parallel Algorithm for Hidden-Surface Removal for Terrains
Abstract. We describe an efficient parallel algorithm for hidden-surface removal for terrain maps. The algorithm runs in O(log 4 n) steps on the CREW PRAM model with a work bound of O((n+k) \polylog ( n)) where n and k are the input and output sizes, respectively. In order to achieve the work bound we use a number of techniques, among which our use of persistent data structures is somewhat novel in the context of parallel algorithms.
32334601
Finding an Approximate Median with High Probability in Constant Parallel Time
Given n elements from a totally ordered set we outline a method for choosing an element whose rank is γn for some 0 0.
11169442
Linear Time Algorithms for Clustering Problems in Any Dimensions
We generalize the k-means algorithm presented by the authors [14] and show that the resulting algorithm can solve a larger class of clustering problems that satisfy certain properties (existence of a random sampling procedure and tightness). We prove these properties for the k-median and the discrete k-means clustering problems, resulting in O(2(k/e)O(1)dn) time (1+e)-approximation algorithms for these problems. These are the first algorithms for these problems linear in the size of the input (nd for n points in d dimensions), independent of dimensions in the exponent, assuming k and e to be fixed. A key ingredient of the k-median result is a (1+e)-approximation algorithm for the 1-median problem which has running time O(2(1/e)O(1)d). The previous best known algorithm for this problem had linear running time.
9302549
Planar Graph Blocking for External Searching

AbstractWe present a new scheme for storing a planar graph in external memory so that any online path can be traversed in an I-O efficient way. Our storage scheme significantly improves the previous results for planar graphs with bounded face size. We also prove an upper bound on I-O efficiency of any storage scheme for well-shaped triangulated meshes. For these meshes, our storage scheme achieves optimal performance.

10562059
Improved decremental algorithms for maintaining transitive closure and all-pairs shortest paths
We present improved algorithms for maintaining transitive closure and all-pairs shortest paths/distances in a digraph under deletion of edges.(MATH) For the problem of transitive closure, the previous best known algorithms, for achieving O(1) query time, require O(\min(m, \frac{n^3}{m}))$ amortized update time, implying an upper bound of O(n^{\frac{3}{2}})$ on update time per edge-deletion. We present an algorithm that achieves $O(1)$ query time and O(n \log^2n + \frac{n^2}{\sqrt{m}}{\sqrt{\log n}})$ update time per edge-deletion, thus improving the upper bound to O(n^{\frac{4}{3}}\sqrt[3]{\log n})$.(MATH) For the problem of maintaining all-pairs shortest distances in unweighted digraph under deletion of edges, we present an algorithm that requires O(\frac{n^3}{m} \log^2 n)$ amortized update time and answers a distance query in O(1) time. This improves the previous best known update bound by a factor of log n. For maintaining all-pairs shortest paths, we present an algorithm that achieves O(\min(n^{\frac{3}{2}} \sqrt{\log n}, \frac{n^3}{m} \log ^2n))$ amortized update time and reports a shortest path in optimal time (proportional to the length of the path). For the latter problem we improve the worst amortized update time bound by a factor of O(\sqrt{\frac{n}{\log n}})$.(MATH) We also present the first decremental algorithm for maintaining all-pairs (1+ε) approximate shortest paths/distances, for any ε > 0, that achieves a sub-quadratic update time of O(n log2n + \frac{n^2}{\sqrt{\epsilon m}}\sqrt{\log n})$ and optimal query time.Our algorithms are randomized and have one-sided error for query (with probability O(1/nc) for any constant c).
1281859
Unsupervised Surrogate Agents and Search Bias Change in Flexible Distributed Scheduling
Computational infrastructures for cooperative work should contain embedded agents for handling many routine tasks (Galegher, Kraut, Egldo 1990), but as the number of agents increases and the agents become geographically and/or conceptually dispersed, supervision of the agents will become increasingly problematic. We argue that agents should be provided with deep domain knowledge that allows them to make justifiable decisions, rather than shallow models of users to mimic. In this paper, we use the application domain of distributed meeting scheduling to investigate how agents embodying deeper domain knowledge can choose among alternative strategies for searching their calendars in order to create flexible schedules within reasonable cost.
3529746
Effects of misconception on reciprocative agents
In open systems, agents act to serve their self-interests instead of working towards group goals. We investigate the choice of interaction strategies and environmental characteristics that will make the best self-interested actions to be cooperative in nature. In our previous work, we have presented a probabilistic reciprocity mechanism that produces stable, cooperative behavior among a group of self-interested agents. The resultant system was found to exhibit close to optimal throughput with a fair distribution of the workload among the participating agents. In this paper, we test the robustness of this scheme by changing some of the assumptions used before. In particular, we evaluate the performance of reciprocative agents when an agent receiving help from another agent underestimates the cost incurred by the helping agent. This kind of misconceptions can derail cooperative processes leading to disintegration of stable groups. Results from experiments with several kinds of misconceptions , however, testify to the robustness of the reciprocative strategy. 1 Introduction Researchers involved in the design of intelligent agents that will interact with other agents in an open, distributed system are faced with the challenge of modeling other agents and their behavior 5]. If one can can assume that all agents will be cooperative in nature, eecient mechanisms can be developed to take advantage of mutual cooperation. But, in an open system, assumptions about cooperative agents or system-wide common goals are hard to justify. More often, we will nd diierent agents have diierent goals and motivations and no real inclination to help another agent achieve its objectives. We assume agents to be self-motivated in their interactions with other agents, and that interacting agents are uniquely identiiable. An agent may help others in performing tasks. We develop a criteria for an agent to decide to help or not to help another agent when the latter requests for help. The decision criteria should be such that it al
5984823
The evolution and stability of cooperative traits
Recent works in multi-agent systems have identified agent behaviors that can develop and sustain mutually beneficial cooperative relationships with like-minded agents and can resist exploitation from selfish agents. Researchers have proposed the use of a probabilistic reciprocity scheme that uses summary information from past interactions to decide whether or not to honor a request for help from another agent. This behavior has been found to be close to optimal in homogeneous groups and outperform exploiters in mixed groups. A major shortcoming of these experiments, however, is that the composition of the group in term of agent behaviors is fixed. We believe that real-life rational agents, on the contrary, will change their behaviors based on observed performances of different behavioral traits with the goal of maximizing performance. In this paper, we present results from experiments on two distinct domains with population groups whose behavioral composition changes based on the performance of the agents. Based on the experimental results, we identify ecological niches for variants of exploitative selfish agents and robust reciprocative agents.
1456313
Fast Concurrent Reinforcement Learners
When several agents learn concurrently, the payoff received by an agent is dependent on the behavior of the other agents. As the other agents learn, the reward of one agent becomes non-stationary. This makes learning in multiagent systemsmore difficult than single-agent learning. A few methods, how-ever, are known to guarantee convergence to equilibrium in the limit in such systems. In this paper we experimentally study one such technique, the minimax-Q, in a competitive domain and prove its equivalence with another well-known method for competitive domains. We study the rate of convergence of minimax-Q and investigate possible ways for increasing the same. We also present a variant of the algorithm, minimax-SARSA, and prove its convergence to minimax-Q values under appropriate conditions. Finally we show that this new algorithm performs better than simple minimax-Q in a general-sum domain as well.
7067374
Profit Sharing Auction
Auctions are a class of multi-party negotiation protocols. Classical auctions try to maximize social welfare by selecting the highest bidder as the winner. If bidders are rational, this ensures that the sum of profits for all bidders and the seller is maximized. In all such auctions, however, only the winner and the seller make any profit. We believe that "social welfare distribution" is a desired goal of any multiparty protocol. In the context of auctions, this goal translates into a rather radical proposal of profit sharing between all bidders and the seller. We propose a Profit Sharing Auction (PSA) where a part of the selling price paid by the winner is paid back to the bidders. The obvious criticism of this mechanism is the incentive for the seller to share its profit with nonwinning bidders. We claim that this loss can be compensated by attracting more bidders to such an auction, resulting in an associated increase in selling price. We run several sets of experiments where equivalent items are concurrently sold at a First Price Sealed Bid, a Vickrey, and a PSA auction. A population of learning bidders repeatedly choose to go to one of these auctions based on their valuation for the good being auctioned and their learned estimates of profits from these auctions. Results show that sellers make more or equivalent profits by using PSA as compared to the classical auctions. Additionally, PSA always attracts more bidders, which might create auxiliary revenue streams, and a desirable lower variability in selling prices. Interestingly then, a rational seller has the incentive to share profits and offer an auction like PSA which maximizes and distributes social welfare.