scholarly journals UUV Autonomous Decision-Making Method Based on Dynamic Influence Diagram

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Hongfei Yao ◽  
Hongjian Wang ◽  
Ying Wang
Keyword(s):  
Decision Making ◽  
Expected Utility ◽  
Situation Awareness ◽  
Threat Assessment ◽  
Expected Utility Theory ◽  
Assessment Model ◽  
Influence Diagram ◽  
Operating Environment ◽  
Autonomous Decision ◽  
Principle Of Maximum

Considering the complexity and uncertainty of decision-making in the operating environment of an unmanned underwater vehicle (UUV), this study proposes an autonomous decision-making method based on the dynamic influence diagram (DID) and expected utility theory. First, a threat assessment model is established for situation awareness of the UUV. Accordingly, a DID model is developed for autonomous decision-making of the UUV. Then, based on the threat assessment results for the UUV, the utility of each decision-making plan in the decision-making nodes is inferred and predicted. Subsequently, the principle of maximum expected utility is used to select an optimal autonomous decision-making plan. Finally, the effectiveness of the DID method is verified by simulation. Compared with the traditional expert systems, the DID system shows great adaptability and exhibits better solutions of dynamic decision problems under uncertainty.

scholarly journals Expected Utility Theory for Monitoring-Based Decision-Making

2016 ◽  
Vol 104 (8) ◽  
pp. 1647-1661 ◽  
Author(s):  
Carlo Cappello ◽  
Daniele Zonta ◽  
Branko Glisic
Keyword(s):  
Decision Making ◽  
Expected Utility ◽  
Utility Theory ◽  
Expected Utility Theory

scholarly journals SDAE+Bi-LSTM-Based Situation Awareness Algorithm for the CAN Bus of Intelligent Connected Vehicles

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 110
Author(s):  
Lei Chen ◽  
Mengyao Zheng ◽  
Zhaohua Liu ◽  
Mingyang Lv ◽  
Lv Zhao ◽  
...  
Keyword(s):  
Situation Awareness ◽  
Can Bus ◽  
Threat Assessment ◽  
Assessment Model ◽  
Risk Level ◽  
Connected Vehicles ◽  
Area Network ◽  
Network Attacks ◽  
The Future ◽  
The Impact

With a deep connection to the internet, the controller area network (CAN) bus of intelligent connected vehicles (ICVs) has suffered many network attacks. A deep situation awareness method is urgently needed to judge whether network attacks will occur in the future. However, traditional shallow methods cannot extract deep features from CAN data with noise to accurately detect attacks. To solve these problems, we developed a SDAE+Bi-LSTM based situation awareness algorithm for the CAN bus of ICVs, simply called SDBL. Firstly, the stacked denoising auto-encoder (SDAE) model was used to compress the CAN data with noise and extract the deep spatial features at a certain time, to reduce the impact of noise. Secondly, a bidirectional long short-term memory (Bi-LSTM) model was further built to capture the periodic features from two directions to enhance the accuracy of the future situation prediction. Finally, a threat assessment model was constructed to evaluate the risk level of the CAN bus. Extensive experiments also verified the improved performance of our SDBL algorithm.

scholarly journals ECONOMICS OF RANDOMNESS: BEHAVIORAL ASPECTS OF DECISION MAKING UNDER RISK

2017 ◽  
Vol 9 (2) ◽  
pp. 7-14
Author(s):  
Alexander Krasilnikov
Keyword(s):  
Decision Making ◽  
Risk Analysis ◽  
Uncertainty Analysis ◽  
Expected Utility ◽  
Utility Theory ◽  
Expected Utility Theory ◽  
Probabilistic Methods ◽  
Risk And Uncertainty ◽  
Decision Making Under Risk ◽  
History Of

The paper discusses evolution of the concept of risk in economics. History of probabilistic methods and approaches to risk and uncertainty analysis is considered. Expected utility theory, behavioral approaches, heuristic models and methods of neuroeconomics are analyzed. Author investigates stability of neoclassical program related to risk analysis and suggests further directions of development.

Measuring Utility, Destabilizing EUT

2018 ◽  
pp. 261-280
Author(s):  
Ivan Moscati
Keyword(s):  
Decision Making ◽  
Behavioral Economics ◽  
Expected Utility ◽  
Utility Theory ◽  
Expected Utility Theory ◽  
Behavioral Models ◽  
Decision Making Under Risk ◽  
Utility Measurement ◽  
Elicitation Methods ◽  
Series Of Experiments

Chapter 16 shows how the validity of expected utility theory (EUT) was increasingly called into question between the mid-1960s and the mid-1970s and discusses how a series of experiments performed from 1974 to 1985 undermined the earlier confidence that EUT makes it possible to measure utility. Beginning in the mid-1960s, in a series of experiments seminal to the field later called behavioral economics, Sarah Lichtenstein, Paul Slovic, Amos Tversky, and others showed that decision patterns violating EUT are systematic. The new experimenters who engaged with the EUT-based measurement of utility from the mid-1970s, namely Uday Karmarkar, Richard de Neufville, Paul Schoemaker, and coauthors, showed that different elicitation methods to measure utility, which according to EUT should produce the same outcome, generate different measures. These findings contributed to destabilizing EUT, undermined the confidence in EUT-based utility measurement, and helped foster a blossoming of novel behavioral models of decision-making under risk.

Marschak and Utility Measurement at Yale

2018 ◽  
pp. 239-246
Author(s):  
Ivan Moscati
Keyword(s):  
Decision Making ◽  
Expected Utility ◽  
Expected Utility Theory ◽  
The Other ◽  
Yale University ◽  
Percent Correct ◽  
History Of ◽  
The 1950S ◽  
The 1960S ◽  
Experimental Findings

Chapter 14 continues the history of the experimental attempts to measure utility by discussing two further experiments performed at Yale University in the early 1960s, one by Trenery Dolbear and the other by Jacob Marschak in association with Gordon Becker and Morris DeGroot. Like the experiments conducted in the 1950s, these were also based on expected utility theory (EUT) and aimed at measuring the utility of money of individuals on the basis of their preferences between gambles where small amounts of money were at stake. There are some differences in the designs of the experiments of the 1950s and those of the 1960s. Like the experimenters of the 1950s, however, Dolbear, Marschak, Becker, and DeGroot also confidently assessed their experimental findings as validating EUT: the theory was not 100 percent correct, but in an approximate sense, it appeared to be an acceptable descriptive theory of decision-making under risk.

From Chicago to Paris

2018 ◽  
pp. 177-192
Author(s):  
Ivan Moscati
Keyword(s):  
Decision Making ◽  
Utility Function ◽  
Expected Utility ◽  
Economic Model ◽  
Expected Utility Theory ◽  
Second Phase ◽  
Cardinal Utility ◽  
Decision Making Under Risk ◽  
The Face

Chapter 11 studies the second phase of the debate on expected utility theory (EUT), which commenced in May 1950, when Paul Samuelson, Leonard J. Savage, Jacob Marschak, Milton Friedman, and William Baumol initiated an intense exchange of letters. These economists argued about the exact assumptions underlying EUT, quarreled over whether these assumptions are compelling requisites for rational behavior under risk, and debated the nature of the cardinal utility function u featured in EUT. This correspondence modified the views of all five economists and transformed Samuelson into a supporter of EUT. In a prominent conference in Paris in May 1952, Friedman, Savage, Marschak, and Samuelson advocated EUT in the face of attacks from Maurice Allais and other opponents of the theory. The Paris conference and the publication of an Econometrica symposium on EUT in October 1952 marked the emergence of EUT as the mainstream economic model of decision-making under risk.

The Road Ahead

2019 ◽  
pp. 229-238
Author(s):  
Richard Pettigrew
Keyword(s):  
Decision Making ◽  
Expected Utility ◽  
Expected Utility Theory ◽  
Rational Decision ◽  
The Road ◽  
Risk Sensitive ◽  
Aggregate Utility ◽  
Imprecise Credences ◽  
Theory Framework

This final chapter of the book summarizes the conclusions of the preceding chapters and looks forward to how the Aggregate Utility Solution might be generalized so that it applies not only to the expected utility theory framework, but also to other frameworks for rational decision-making. In particular, it explains how we might extend the Aggregate Utility Solution to the framework of imprecise credences and utilities, and to the framework of risk-sensitive decision theories.

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