AI Chat AI Image Generator AI Video Text to Speech

Complexity of Computing the Shapley Value in Games with Externalities

09/04/2019

∙

by Oskar Skibski, et al.

∙

∙

We study the complexity of computing the Shapley value in games with externalities. We focus on two representations based on marginal contribution nets (embedded MC-nets and weighted MC-nets). Our results show that while weighted MC-nets are more concise than embedded MC-nets, they have slightly worse computational properties when it comes to computing the Shapley value.

page 1

page 2

page 3

page 4

research

∙ 06/02/2020

Coordinating Multiagent Industrial Symbiosis

We present a formal multiagent framework for coordinating a class of col...

0 Vahid Yazdanpanah, et al. ∙

research

∙ 02/20/2020

Weighted Epsilon-Nets

Motivated by recent work of Bukh and Nivasch on one-sided ε-approximants...

0 Daniel Bertschinger, et al. ∙

research

∙ 12/12/2019

A New Method for Verifying d-MC Candidates

Network reliability modeling and calculation is a very important study d...

0 Wei-Chang Yeh, et al. ∙

research

∙ 11/18/2019

Gamma-Nets: Generalizing Value Estimation over Timescale

We present Γ-nets, a method for generalizing value function estimation o...

31 Craig Sherstan, et al. ∙

research

∙ 05/20/2020

Coverage Analysis of Net Inscriptions in Coloured Petri Net Models

High-level Petri net such as Coloured Petri Nets (CPNs) are characterise...

0 Faustin Ahishakiye, et al. ∙

research

∙ 06/11/2020

Deep Time-Delay Reservoir Computing: Dynamics and Memory Capacity

The Deep Time-Delay Reservoir Computing concept utilizes unidirectionall...

0 Mirko Goldmann, et al. ∙

research

∙ 08/15/2020

Adversarial Filters for Secure Modulation Classification

Modulation Classification (MC) refers to the problem of classifying the ...

0 Alex Berian, et al. ∙