975549
Agent teaching agent framework
We address the problem of knowledge transfer between a trainer and a trainee agent. The knowledge being transferred is a concept description, a boolean-valued function that classifies input examples as members or non-members of the target concept. We assume that the trainer agent does not have access to the internal knowledge representation of the trainee agent, but can evaluate its concept recognition abilities by asking it to categorize selected exemplars and non-exemplars of the target concept. The trainer agent also do not have access to the original training set from which it learned its concept description, and hence has to rely only on that learned knowledge to train the trainee agent. We present an Agent Teaching Agent (ATA) framework which focuses on incremental selection of training examples by the trainer to expedite the learning of the trainee agent. We envisage an iterative training procedure in which alternatively the trainer selects a set of training and testing exemplars, the trainee trains using the training set and then classifies the testing set, the trainer observes errors made by the trainee in classifying the instances in the last testing set and accordingly generates the next training and testing sets. This iterative process converges when error of the trainee falls below a given threshold. We now present these iterative training steps in an algorithmic form:
1062630
The Role of Commitment in Cooperative Negotiation
Cooperative information agents need mechanisms that enable them to work together effectively while solving common problems. We investigate the use of commitment by agents to proposed actions as a mechanism that allow agents to work concurrently on interdependent problems. Judicious use of commitment can not only increase the throughput of cooperative information systems, but also allow them to deal flexibly with dynamically changing environments. We use the domain of distributed scheduling to demonstrate that static commitment strategies are ineffective. Results from simulated experiments are used to identify the environmental features on which an adaptive commitment strategy should be predicated.
12119095
Correlating Internal Parameters and External Performance: Learning Soccer Agents
We have developed a Soccer simulator in Java, which allows us to evaluate agent behavioral strategies for effectively collaborating with other team members while countering threats posed by the opposing team. Whereas action selection mechanisms to decide when to shoot, pass, dribble, guard opponent, tackle, etc. are important from a player's viewpoint, learning the capabilities of individual opponents from repeated encounters can provide critical information for the success of a team. Each agent is described by a set of skill levels for different soccer skills. A soccer agent uses its incomplete perceptions, model of the changing environment, knowledge of the skill levels of its own team players, and estimated skill levels of the opponent team players to select the most prudent action. In this paper, we identify learning opportunities for soccer agents, and investigate some of these possibilities in detail. Initial experimental results demonstrate the advantage of learning agents.
3526092
Selecting partners
1. I N T R O D U C T I O N In an open environment the goal of a self-interested agent would be to interact with or enter into partnership with those agents that will produce maximal local utility for this agent [5]. We assume that an agent can get one of several payoffs (utilities) for joining a particular coalition which is determined by a static probability distribution over the payoffs for each coalition. We consider choosing a single coalition to interact with repeatedly when the total number of interactions is known ahead of time.
482217
Believing others: pro and cons
Under the assumption that agents remain in the system for significant time periods, or that the agent composition changes only slowly Sen (1996) has previously presented a prescriptive strategy for promoting and sustaining cooperation among self-interested agents. The adaptive, probabilistic policy prescribed promotes reciprocative cooperation that improves both individual and group performance in the long run. In the short run, however, selfish agents could still exploit reciprocative agents. In this paper we evaluate the hypothesis that the exploitative tendencies of selfish agents can be effectively curbed if reciprocative agents share their "opinions" of other agents. Since the true nature of agents are not known a priori and is learned from experience, believing others can also pose other hazards. We provide a learned trust-based evaluation function that is shown to resist both individual and concerted deception on the part of selfish agents.
43158386
Evolution and learning in multiagent systems
1681173
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.
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.
11397521
Nearest neighbors search using point location in balls with applications to approximate Voronoi decompositions
We present alternate reductions of the nearest neighbor searching problem to Point Location in Balls that reduces the space bound of Sariel Har-Peled's [S. Har-Peled, A replacement for Voronoi diagrams of near linear size, in: Proc. of IEEE FOCS, 2001, pp. 94-103, full version available from http://www.uiuc.edu/~sariel/papers] recent result on Approximate Voronoi Diagrams to linear while maintaining the logarithmic search time. We do this by simplifying the construction of [S. Har-Peled, A replacement for Voronoi diagrams of near linear size, in: Proc. of IEEE FOCS, 2001, pp. 94-103, full version available from http://www.uiuc. edu/~sariel/papers] that reduces the number of balls generated by algorithm by a logarithmic factor to O(n log n). We further reduce the number of balls by a new hierarchical decomposition scheme and a generalization of PLEBs to achieve linear space decomposition for nearest neighbor searching. The construction of our data structures takes O(n log n) time.
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>>