Value Functions

2015 ◽  
pp. 81-133
Author(s):  
Jean-Pierre Tignol ◽  
Adrian R. Wadsworth
Keyword(s):  
Value Functions

The Received Wisdom

2018 ◽  
Author(s):  
Meghan Sullivan
Keyword(s):  
Temporal Discounting ◽  
Value Functions ◽  
Distant Future ◽  
Rational Planning ◽  
Future Discounting ◽  
The Future

This chapter introduces the reader to future discounting and some received wisdom. The received wisdom about rational planning tends to assume that it is irrational to have near‐biased preferences (i.e., preferences for lesser goods now compared to greater goods further in the future).Thechapter describes these preferences by introducing the reader to value functions. Value functions are then used to model different kinds of distant future temporal discounting (e.g., hyperbolic, exponential, absolute). Finally, the chapter makes a distinction between temporal discounting and risk discounting. It offers a reverse lottery test to tease apart these two kinds of discounting.

Value functions and two classes of binary codes

2021 ◽  
Vol 344 (3) ◽  
pp. 112261
Author(s):  
Zihui Liu
Keyword(s):  
Binary Codes ◽  
Value Functions

scholarly journals Assessing the Performance of Interactive Multiobjective Optimization Methods

2021 ◽  
Vol 54 (4) ◽  
pp. 1-27
Author(s):  
Bekir Afsar ◽  
Kaisa Miettinen ◽  
Francisco Ruiz
Keyword(s):  
Multiobjective Optimization ◽  
Decision Maker ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Interactive Methods ◽  
Value Functions ◽  
Technical Aspects ◽  
Preference Information ◽  
Multiobjective Optimization Problems ◽  
Human Decision

Interactive methods are useful decision-making tools for multiobjective optimization problems, because they allow a decision-maker to provide her/his preference information iteratively in a comfortable way at the same time as (s)he learns about all different aspects of the problem. A wide variety of interactive methods is nowadays available, and they differ from each other in both technical aspects and type of preference information employed. Therefore, assessing the performance of interactive methods can help users to choose the most appropriate one for a given problem. This is a challenging task, which has been tackled from different perspectives in the published literature. We present a bibliographic survey of papers where interactive multiobjective optimization methods have been assessed (either individually or compared to other methods). Besides other features, we collect information about the type of decision-maker involved (utility or value functions, artificial or human decision-maker), the type of preference information provided, and aspects of interactive methods that were somehow measured. Based on the survey and on our own experiences, we identify a series of desirable properties of interactive methods that we believe should be assessed.

scholarly journals Sensitivity Analysis for Two-Level Value Functions with Applications to Bilevel Programming

2012 ◽  
Vol 22 (4) ◽  
pp. 1309-1343 ◽  
Author(s):  
S. Dempe ◽  
B. S. Mordukhovich ◽  
A. B. Zemkoho
Keyword(s):  
Sensitivity Analysis ◽  
Bilevel Programming ◽  
Value Functions

scholarly journals Bayesian Quickest Detection Problems for Some Diffusion Processes

2013 ◽  
Vol 45 (1) ◽  
pp. 164-185 ◽  
Author(s):  
Pavel V. Gapeev ◽  
Albert N. Shiryaev
Keyword(s):  
Diffusion Processes ◽  
Free Boundary Problems ◽  
Three Dimensional ◽  
Parabolic Type ◽  
Value Functions ◽  
Weighted Likelihood ◽  
Likelihood Ratios ◽  
Boundary Problems ◽  
Detection Delay ◽  
Penalty Costs

We study the Bayesian problems of detecting a change in the drift rate of an observable diffusion process with linear and exponential penalty costs for a detection delay. The optimal times of alarms are found as the first times at which the weighted likelihood ratios hit stochastic boundaries depending on the current observations. The proof is based on the reduction of the initial problems into appropriate three-dimensional optimal stopping problems and the analysis of the associated parabolic-type free-boundary problems. We provide closed-form estimates for the value functions and the boundaries, under certain nontrivial relations between the coefficients of the observable diffusion.

Rule Fitness and Pathology in Learning Classifier Systems

2004 ◽  
Vol 12 (1) ◽  
pp. 99-135 ◽  
Author(s):  
Tim Kovacs
Keyword(s):  
Value Functions ◽  
Classifier Systems ◽  
Reward Function ◽  
Learning Classifier ◽  
Learning Tasks ◽  
General Rules ◽  
Strength Based ◽  
The Difference ◽  
The Value Function ◽  
Do So

It has long been known that in some relatively simple reinforcement learning tasks traditional strength-based classifier systems will adapt poorly and show poor generalisation. In contrast, the more recent accuracy-based XCS, appears both to adapt and generalise well. In this work, we attribute the difference to what we call strong over general and fit over general rules. We begin by developing a taxonomy of rule types and considering the conditions under which they may occur. In order to do so an extreme simplification of the classifier system is made, which forces us toward qualitative rather than quantitative analysis. We begin with the basics, considering definitions for correct and incorrect actions, and then correct, incorrect, and overgeneral rules for both strength and accuracy-based fitness. The concept of strong overgeneral rules, which we claim are the Achilles' heel of strength-based classifier systems, are then analysed. It is shown that strong overgenerals depend on what we call biases in the reward function (or, in sequential tasks, the value function). We distinguish between strong and fit overgeneral rules, and show that although strong overgenerals are fit in a strength-based system called SB-XCS, they are not in XCS. Next we show how to design fit overgeneral rules for XCS (but not SB-XCS), by introducing biases in the variance of the reward function, and thus that each system has its own weakness. Finally, we give some consideration to the prevalence of reward and variance function bias, and note that non-trivial sequential tasks have highly biased value functions.

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