4,760 research outputs found
Complementary cooperation, minimal winning coalitions, and power indices
We introduce a new simple game, which is referred to as the complementary
weighted multiple majority game (C-WMMG for short). C-WMMG models a basic
cooperation rule, the complementary cooperation rule, and can be taken as a
sister model of the famous weighted majority game (WMG for short). In this
paper, we concentrate on the two dimensional C-WMMG. An interesting property of
this case is that there are at most minimal winning coalitions (MWC for
short), and they can be enumerated in time , where is the
number of players. This property guarantees that the two dimensional C-WMMG is
more handleable than WMG. In particular, we prove that the main power indices,
i.e. the Shapley-Shubik index, the Penrose-Banzhaf index, the Holler-Packel
index, and the Deegan-Packel index, are all polynomially computable. To make a
comparison with WMG, we know that it may have exponentially many MWCs, and none
of the four power indices is polynomially computable (unless P=NP). Still for
the two dimensional case, we show that local monotonicity holds for all of the
four power indices. In WMG, this property is possessed by the Shapley-Shubik
index and the Penrose-Banzhaf index, but not by the Holler-Packel index or the
Deegan-Packel index. Since our model fits very well the cooperation and
competition in team sports, we hope that it can be potentially applied in
measuring the values of players in team sports, say help people give more
objective ranking of NBA players and select MVPs, and consequently bring new
insights into contest theory and the more general field of sports economics. It
may also provide some interesting enlightenments into the design of
non-additive voting mechanisms. Last but not least, the threshold version of
C-WMMG is a generalization of WMG, and natural variants of it are closely
related with the famous airport game and the stable marriage/roommates problem.Comment: 60 page
Deep Reinforcement Learning for Join Order Enumeration
Join order selection plays a significant role in query performance. However,
modern query optimizers typically employ static join enumeration algorithms
that do not receive any feedback about the quality of the resulting plan.
Hence, optimizers often repeatedly choose the same bad plan, as they do not
have a mechanism for "learning from their mistakes". In this paper, we argue
that existing deep reinforcement learning techniques can be applied to address
this challenge. These techniques, powered by artificial neural networks, can
automatically improve decision making by incorporating feedback from their
successes and failures. Towards this goal, we present ReJOIN, a
proof-of-concept join enumerator, and present preliminary results indicating
that ReJOIN can match or outperform the PostgreSQL optimizer in terms of plan
quality and join enumeration efficiency
Hybrid behavioural-based multi-objective space trajectory optimization
In this chapter we present a hybridization of a stochastic based search approach for multi-objective optimization with a deterministic domain decomposition of the solution space. Prior to the presentation of the algorithm we introduce a general formulation of the optimization problem that is suitable to describe both single and multi-objective problems. The stochastic approach, based on behaviorism, combinedwith the decomposition of the solutions pace was tested on a set of standard multi-objective optimization problems and on a simple but representative case of space trajectory design
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