3543479
Influence of perspectives on help-giving behaviors
We have developed and analyzed probabilistic reciprocity schemes as strategies to be used by self-interested agents to decide on whether or not to help other agents [2]. The goal is to develop strategies to make self-interested agents cooperate. Such cooperation will improve the overall system performance when each cooperative act produces a net savings for the system(the helping agent incurs less cost than savings obtained by the helped agent). Probabilistic reciprocity strategies are considerably more sophisticated than deterministic reciprocity schemes like tit-for-tat [l] and avoids major problems associated with the latter schemes [3]. We have experimentally shown that the probabilistic reciprocity strategy leads to close to optimal individual and group performance, when agents interact repeatedly over a long period of time. This happens when group composition changes only slowly. Additionally, this strategy is stable against selfish intruders. In this paper, we analyze the problem of the use of the right perspective to calculate the balance of help even under complete information. In general, the cost(&) incurred by the helping agent will differ from the savings(Sh) obtained by the helped agent. Which of these values should be used to update the balance of help between the two agents? If the helping agent increase its balance of work with the helped agent by Sh, rather than by Ch, it can expect more help from the helped agent in future. But this approach can lead to ‘cancelations of help’ leading to no system-wide or individual benefits for cooper&ive behavior. The answer to this question fundamentally affects the emergence of cooperative behavior in self-interested agent groups.
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.
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.
43158386
Evolution and learning in multiagent systems
15974834
Learning Pareto-optimal Solutions in 2x2 Conflict Games
Multiagent learning literature has investigated iterated two-player games to develop mechanisms that allow agents to learn to converge on Nash Equilibrium strategy profiles. Such equilibrium configurations imply that no player has the motivation to unilaterally change its strategy. Often, in general sum games, a higher payoff can be obtained by both players if one chooses not to respond myopically to the other player. By developing mutual trust, agents can avoid immediate best responses that will lead to a Nash Equilibrium with lesser payoff. In this paper we experiment with agents who select actions based on expected utility calculations that incorporate the observed frequencies of the actions of the opponent(s). We augment these stochastically greedy agents with an interesting action revelation strategy that involves strategic declaration of one's commitment to an action to avoid worst-case, pessimistic moves. We argue that in certain situations, such apparently risky action revelation can indeed produce better payoffs than a non-revealing approach. In particular, it is possible to obtain Pareto-optimal Nash Equilibrium outcomes. We improve on the outcome efficiency of a previous algorithm and present results over the set of structurally distinct two-person two-action conflict games where the players' preferences form a total order over the possible outcomes. We also present results on a large number of randomly generated payoff matrices of varying sizes and compare the payoffs of strategically revealing learners to payoffs at Nash equilibrium.
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.
207161263
tagging, communities, vocabulary, evolution
A tagging community's vocabulary of tags forms the basis for social navigation and shared expression.We present a user-centric model of vocabulary evolution in tagging communities based on community influence and personal tendency. We evaluate our model in an emergent tagging system by introducing tagging features into the MovieLens recommender system.We explore four tag selection algorithms for displaying tags applied by other community members. We analyze the algorithms 'effect on vocabulary evolution, tag utility, tag adoption, and user satisfaction.
2513432
Cache-efficient matrix transposition
We investigate the memory system performance of several algorithms for transposing an N/spl times/N matrix in-place, where N is large. Specifically, we investigate the relative contributions of the data cache, the translation lookaside buffer, register tiling, and the array layout function to the overall running time of the algorithms. We use various memory models to capture and analyze the effect of various facets of cache memory architecture that guide the choice of a particular algorithm, and attempt to experimentally validate the predictions of the model. Our major conclusions are as follows: limited associativity in the mapping from main memory addresses to cache sets can significantly degrade running time; the limited number of TLB entries can easily lead to thrashing; the fanciest optimal algorithms are not competitive on real machines even at fairly large problem sizes unless cache miss penalties are quite high: low-level performance tuning "hacks", such as register tiling and array alignment, can significantly distort the effects of improved algorithms; and hierarchical non-linear layouts are inherently superior to the standard canonical layouts (such as row- or column-major) for this problem.
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.
7431315
Optimal, Output-sensitive Algorithms for Constructing Planar Hulls in Parallel
Abstract In this paper we focus on the problem of designing very fast parallel algorithms for the planar convex hull problem that achieve the optimal O(nlogH) work-bound for input size n and output size H. Our algorithms are designed for the arbitrary CRCW PRAM model. We first describe a very simple O(lognlogH) time optimal deterministic algorithm for the planar hulls which is an improvement over the previously known Ω( log 2 n) time algorithm for small outputs. For larger values of H, we can achieve a running time of O(lognloglogn) steps with optimal work. We also present a fast randomized algorithm that runs in expected time O(logH·loglogn) and does optimal O(nlogH) work. For log H = Ω( loglog n) , we can achieve the optimal running time of O(logH) while simultaneously keeping the work optimal. When logH is o(logn), our results improve upon the previously best known Θ(logn) expected time randomized algorithm of Ghouse and Goodrich. The randomized algorithms do not assume any input distribution and the running times hold with high probability.
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.