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.
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.
16671606
Individual learning of coordination knowledge
Abstract. Social agents, both human and computational, inhabiting a world containing multiple active agents, need to coordinate their activities. This is because agents share resources, and without proper coordination or ‘rules of the road’, everybody will be interfering with the plans of others. As such, we need coordination schemes that allow agents to effectively achieve local goals without adversely affecting the problem-solving capabilities of other agents. Researchers in the field of Distributed Artificial Intelligence (DAI) have developed a variety of coordination schemes under different assumptions about agent capabilities and relationships. Whereas some of these researchers have been motivated by human cognitive biases, others have approached it as an engineering problem of designing the most effective coordination architecture or protocol. We evaluate individual and concurrent learning by multiple, autonomous agents as a means for acquiring coordination knowledge. We show that a uniform reinforc...
42428189
Lower Bounds for Parallel Algebraic Decision Trees, Complexity of Convex Hulls and Related Problems
We show that any parallel algorithm in the fixed degree algebraic decision tree model that answers membership queries in W ⊑ R n using p processors, requires Ω(¦W¦/n log(p/n)) rounds where ¦W¦ is the number of connected components of W. We further prove a similar result for the average case complexity. We give applications of this result to various fundamental problems in computational geometry like convex-hull construction and trapezoidal decomposition and also present algorithms with matching upper bounds.
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.
7580001
Evolutionary Tournament-Based Comparison of Learning and Non-Learning Strategies for Iterated Games
Evolutionary tournaments have been used as a tool for comparing strategies. For instance, in the late 1970’s, Axelro d organized tournaments to compare strategies for playing th e iterated prisoner’s dilemma (PD) game. While these tournaments and later research have provided us with a better understanding of successful strategies for iterated PD, ou r understanding is less clear about strategies for playing it erated versions of arbitrary single-stage games. While solut ion concepts like Nash equilibria has been proposed for general sum games, learning strategies like fictitious play may be pr eferred for playing against sub-rational players. In this pa per, we discuss the relative performance of both learning and non learning strategies that embody some of the above approache s on an experimental testbed of all possible structurally dis tinct 2x2 conflicted games with ordinal payoffs. This set of bimatrices provides a baseline, neutral testbed for comparin g strategies. We discuss the testbed, our choice of represent ative learning and non-learning strategies and relative ran kings of these strategies ranked by cumulative score in tournamen ts. We also study the performance of the strategies in an evolutionary tournament. Finally, we provide some analysis of th e observed results to highlight the advantage of learning str ategies.