16723670
PLEASE: A Prototype Learning System Using Genetic Algorithms
Prototypes have been proposed as representation of concepts that are used eeec-tively by humans. Developing computational schemes for generating prototypes from examples , however, has proved to be a dii-cult problem. We present a novel genetic algorithm based prototype learning system, PLEASE, for constructing appropriate prototypes from classiied training instances. After constructing a set of prototypes for each of the possible classes, the class of a new input instance is determined by the nearest prototype to this instance. Attributes are assumed to be ordinal in nature 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. We present experimental results on a series of artiicial problems of varying complexity. PLEASE performs competitively with several nearest neighbor clas-siication algorithms on the problem set. An analysis of the strengths and weaknesses of the initial version of our system motivates the need for additional operators. The inclusion of these operators substantially improves the performance of the system on particularly diicult problems.
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>>
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.
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.
14273924
The Success and Failure of Tag-Mediated Evolution of Cooperation
Use of tags to limit partner selection for playing has been shown to produce stable cooperation in agent populations playing the Prisoner's Dilemma game. There is, however, a lack of understanding of how and why tags facilitate such cooperation. We start with an empirical investigation that identifies the key dynamics that result in sustainable cooperation in PD. Sufficiently long tags are needed to achieve this effect. A theoretical analysis shows that multiple simulation parameters including tag length, mutation rate and population size will have significant effect on sustaining cooperation. Experiments partially validate these observations. Additionally, we claim that tags only promote mimicking and not coordinated behavior in general, i.e., tags can promote cooperation only if cooperation requires identical actions from all group members. We illustrate the failure of the tag model to sustain cooperation by experimenting with domains where agents need to take complementary actions to maximize payoff.
10126037
PDM sorting algorithms that take a small number of passes
We live in an era of data explosion that necessitates the discovery of novel out-of-core techniques. The I/O bottleneck has to be dealt with in developing out-of-core methods. The parallel disk model (PDM) has been proposed to alleviate the I/O bottleneck. Sorting is an important problem that has ubiquitous applications. Several asymptotically optimal PDM sorting algorithms are known and now the focus has shifted to developing algorithms for problem sizes of practical interest. In this paper we present several novel algorithms for sorting on the PDM that take only a small number of passes through the data. We also present a generalization of the zero-one principle for sorting. A shuffling lemma is presented as well. These lemmas should be of independent interest for average case analysis of sorting algorithms as well as for the analysis of randomized sorting algorithms.
47152006
Optimal proxy cache allocation for efficient streaming media distribution
We address the problem of efficiently streaming a set of heterogeneous videos from a remote server through a proxy to multiple asynchronous clients so that they can experience playback with low startup delays. We determine the optimal proxy prefix cache allocation to the videos that minimizes the aggregate network bandwidth cost. We integrate proxy caching with traditional server-based reactive transmission schemes such as hatching, patching and stream merging to develop a set of proxy-assisted delivery schemes. We quantitatively explore the impact of the choice of transmission scheme, cache allocation policy, proxy cache size, and availability of unicast versus multicast capability, on the resulting transmission cost. Our evaluations show that even a relatively small prefix cache (10%-20% of the video repository) is sufficient to realize substantial savings in transmission cost. We find that carefully designed proxy-assisted reactive transmission schemes can produce significant cost savings even in a predominantly unicast environment such as the Internet.
14976465
Using Inertia and Referrals to Facilitate Satisficing Distributions
We study the problem of distributed, self-interested agents searching for high-quality service providers where the performance of a service provider depends on its work load. Agents use referrals from peers to locate satisfactory providers. While stable environments may facilitate fast convergence to satisfying states, greedy and myopic behaviors by distributed agents can lead to poor and variable performances for the entire community. We present mechanisms for resource discovery that involve learning, over interactions, both the performance levels of different service providers as well as the quality of referrals provided by other agents. We study parameters controlling system performance to better comprehend the reasons behind the observed performances of the proposed coordination schemes.
10902519
Effects of Local Information on Group Behavior
Researchers in the field of Distributed Artificial Intelligence have studied the effects of local decisionmaking on overall system performance in both cooperative and self-interested agent groups (Bond & Gasser, 1988) . The performance of individual agents depends critically on the quality of information available to it about local and global goals and resources. Whereas in general it is assumed that the more accurate and up-to-date the available information, the better is the expected performance of the individual and the group, this conclusion can be challenged in a number of scenarios. The populace in human societies tend to look for opportunities and search for better opportunities in their environment (Bartos, 1967) . The theory of migration in social behavior and occupational mobility suggests that the stability of the population depends on how an individual chooses its action based on the prevailing circumst antes. As agent designers, we are faced with the problem of developing decision mechanisms that allow agent societies to stabilize in states where system resources are effectively utilized. In this research, we focus on a particular aspect of distributed decision-making: the effect of limited global knowledge on group behavior. The research question that we are asking is the following: Can limited local knowledge be a boon rather than a bane in a multiagent system ? To investigate this issue we use a resource utilization problem where a number of agents are distributed between several identical resources. We assume that the cost of using any resource is directly proportional to its usage. This cost can be due to a delay in processing of the task in hand, or a reduction in the quality of the resource due to congestion. Hence, there is a justified urge in agents to seek out and move to resource with lesser usage. Other researchers have shown that such systems can exhibit oscillatory or chaotic behavior where agents move back and forth between resources (Hogg & Huberman 1991; Kephart, Hogg & Huberman 1989) resulting in ineffective uti1406 AAAI-96 lization of system resources. Whereas asynchrony, heterogeneous agent decision mechanisms, etc. has been suggested as possible means for solving the instability problem, our proposed solution of using locally differing global views is a novel mechanism to introduce asymmetry in group decisions that expedites group stability.
24136651
Optimal randomized parallel algorithms for computational geometry
We present parallel algorithms for some fundamental problems in computational geometry which have a running time ofO(logn) usingn processors, with very high probability (approaching 1 asn → ∞). These include planar-point location, triangulation, and trapezoidal decomposition. We also present optimal algorithms for three-dimensional maxima and two-set dominance counting by an application of integer sorting. Most of these algorithms run on a CREW PRAM model and have optimal processor-time product which improve on the previously best-known algorithms of Atallah and Goodrich [5] for these problems. The crux of these algorithms is a useful data structure which emulates the plane-sweeping paradigm used for sequential algorithms. We extend some of the techniques used by Reischuk [26] and Reif and Valiant [25] for flashsort algorithm to perform divide and conquer in a plane very efficiently leading to the improved performance by our approach.