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A hybrid COAε-constraint method for solving multi-objective problems
In this paper, a hybrid method for solving multi-objective problem has b...
09/25/2015 ∙ by Mahdi parvizi, et al. ∙
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Order-of-Magnitude Influence Diagrams
In this paper, we develop a qualitative theory of influence diagrams tha...
02/14/2012 ∙ by Radu Marinescu, et al. ∙
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A utility-based analysis of equilibria in multi-objective normal form games
In multi-objective multi-agent systems (MOMAS), agents explicitly consid...
01/17/2020 ∙ by Roxana Rădulescu, et al. ∙
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Hybrid Influence Diagrams Using Mixtures of Truncated Exponentials
Mixtures of truncated exponentials (MTE) potentials are an alternative t...
07/12/2012 ∙ by Barry Cobb, et al. ∙
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Targeting Solutions in Bayesian Multi-Objective Optimization: Sequential and Parallel Versions
Multi-objective optimization aims at finding trade-off solutions to conf...
11/09/2018 ∙ by David Gaudrie, et al. ∙
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Multi-Objective Mixed Integer Programming: An Objective Space Algorithm
This paper introduces the first objective space algorithm which can exac...
09/09/2019 ∙ by William Pettersson, et al. ∙
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Unconstrained Influence Diagrams
We extend the language of influence diagrams to cope with decision scena...
12/12/2012 ∙ by Finn Verner Jensen, et al. ∙
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Multi-objective Influence Diagrams
10/16/2012 ∙ by Radu Marinescu, et al. ∙
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We describe multi-objective influence diagrams, based on a set of p objectives, where utility values are vectors in Rp, and are typically only partially ordered. These can still be solved by a variable elimination algorithm, leading to a set of maximal values of expected utility. If the Pareto ordering is used this set can often be prohibitively large. We consider approximate representations of the Pareto set based on e-coverings, allowing much larger problems to be solved. In addition, we define a method for incorporating user tradeoffs, which also greatly improves the efficiency.
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