14600509
A Genetic Prototype Learner
Supervised classification problems have received considerable attention from the machine learning community. We propose a novel genetic algorithm based prototype learning system, PLEASE, for this class of problems. Given a set of prototypes for each of the possible classes, the class of an input instance is determined by the prototype nearest to this instance. We assume ordinal attributes and prototypes are represented as sets of feature-value pairs. A genetic algorithm is used to evolve the number of prototypes per class and their positions on the input space as determined by corresponding feature-value pairs. Comparisons with C4.5 on a set of artificial problems of controlled complexity demonstrate the effectiveness of the proposed system.
9417710
Using real-valued genetic algorithms to evolve rule sets for classification
In this paper, we use a genetic algorithm to evolve a set of classification rules with real-valued attributes. We show how real-valued attribute ranges could be encoded with real-valued genes and present a new uniform method for representing don't cares in the rules. We view supervised classification as an optimization problem, and evolve rule sets that maximize the number of correct classifications of input instances. We use a variant of the Pitt approach to genetic-based machine learning system with a novel conflict resolution mechanism between competing rules within the same rule set. Experimental results demonstrate the effectiveness of our proposed approach on a benchmark wine classifier system.<<ETX>>
45367497
Resolving Social Dilemmas Using Genetic Algorithms: Initial Results
13878075
Optimal sequencing of individually rational contracts
Multiagent researchers have worked on the problem of determining optimal contracts between self-interested agents. In particular, Sandholm et al. [1,8] have both theoretically and experimentally studied the necessity and usefulness of different contract types under the assumption of individually myopically rational contracting. We study a variant of sequential contracting where the goal is to maximize social welfare through a fixed-length sequence of individually rational contracts. The space of possible contract sequences is exponential. We compare a greedy deterministic heuristic with a stochastic genetic algorithm based approach for this optimal sequential contract selection problem. We focus on sub-additive domains where individually rational contracts are feasible with side payments. We show that the GA-based approach consistently outperforms the deterministic heuristic by generating larger social welfare.
14171501
A tale of two representations
Attribute based concept learning has been an active area of machine learning research over the past decade. A number of proposed concept learners are rule-based systems, whose eeciency depends largely on the representation of the rules. We analyze two attribute-value rule representations for the compactness of concept deenition. A spectrum of concepts, with diierent numbers of conjuncts and disjuncts in their deenition, is used to evaluate the eeectiveness of the chosen representations. Our analysis shows that neither of the representations is dominant over the other, and it is beneecial to switch representations with varying concept complexity. Experimental veriication of the predicted tradeoo is provided. The relatively novel of the two representations is found to be better, in general, at concisely representing concepts than the more commonly used representation. We present a formula to choose between these representations, which uses an estimate of the complexity of the concept to be learnt.
5317468
Strongly Typed Genetic Programming in Evolving Cooperation Strategies
A key concern in genetic programming GP is the size of the state space which must be searched for large and complex problem do mains One method to reduce the state space size is by using Strongly Typed Genetic Programming STGP We applied both GP and STGP to construct cooperation strate gies to be used by multiple predator agents to pursue and capture a prey agent on a grid world This domain has been extensively studied in Distributed Arti cial Intelligence DAI as an easy to describe but di cult to solve cooperation problem The evolved programs from our systems are competitive with manually derived greedy algorithms In particular the STGP paradigm evolved strategies in which the predators were able to achieve their goal without explicitly sens ing the location of other predators or com municating with other predators This is an improvement over previous research in this area The results of our experiments indicate that STGP is able to evolve programs that perform signi cantly better than GP evolved programs In addition the programs gener ated by STGP were easier to understand
2290480
Optimal multicast smoothing of streaming video over an internetwork
A number of applications such as internet video broadcasts, corporate telecasts, distance learning etc. require transmission of streaming video to multiple simultaneous users across an internetwork. The high bandwidth requirements coupled with the multi-timescale burstiness of compressed video make it a challenging problem to provision network resources for transmitting streaming multimedia. For such applications to become affordable and ubiquitous, it is necessary to develop scalable techniques which can efficiently deliver streaming video to multiple heterogeneous clients across a heterogeneous internetwork. We propose using multicasting of smoothed video and differential caching of the video at intermediate nodes in the distribution tree, as techniques for reducing the network bandwidth requirements of such dissemination. We formulate the multicast smoothing problem, and develop an algorithm for computing the set of optimally smoothed transmission schedules for the tree (such that the transmission schedule along each link in the tree has the lowest peak rate and rate variability for any feasible transmission schedule for that link) given a buffer allocation to the different nodes in the tree. We also develop an algorithm to compute the minimum total buffer allocation to the entire tree and the corresponding allocation to each node, such that feasible transmission is possible to all the clients, when the tree has heterogeneous rate constraints. MPEG-2 trace-driven performance evaluations indicate that there are substantial benefits from multicast smoothing and differential caching. For example, the optimal multicast smoothing can reduce the total transmission bandwidth requirements in the distribution tree by more than a factor of 3 as compared to multicasting the unsmoothed stream.
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.
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.
6882401
A Simple Optimal Randomized Algorithm for Sorting on the PDM
The Parallel Disks Model (PDM) has been proposed to alleviate the I/O bottleneck that arises in the processing of massive data sets. Sorting has been extensively studied on the PDM model due to the fundamental nature of the problem. Several randomized algorithms are known for sorting. Most of the prior algorithms suffer from undue complications in memory layouts, implementation, or lack of tight analysis. In this paper we present a simple randomized algorithm that sorts in optimal time with high probablity and has all the desirable features for practical implementation.
10373451
Reciprocal resource sharing in P2P environments
Peer-to-peer (P2P) systems enable users to share resources in a networked environment without worrying about issues such as scalability and load balancing. Unlike exchange of goods in a traditional market, resource exchange in P2P networks does not involve monetary transactions. This makes P2P systems vulnerable to problems including the free-rider problem that enables users to acquire resources without contributing anything, collusion between groups of users to incorrectly promote or malign other users, and zero-cost identity that enables nodes to obliterate unfavorable history without incurring any expenditure. Previous research addresses these issues using user-reputation, referrals, and shared history based techniques. Here, we describe a multi-agent based reciprocity mechanism where each user's agent makes the decision to share a resource with a requesting user based on the amount of resources previously provided by the requesting user to the providing user and globally in the system. A robust reputation mechanism is proposed to avoid the differential exploitations by the free-riders and to prevent collusion. Experimental results on a simulated P2P network addresses the problems identified above and shows that users adopting the reciprocative mechanism outperform users that do not share resources in the P2P network. Hence, our proposed reciprocative mechanism effectively suppresses free-riding.