scholarly journals On Cognitive Searching Optimization in Semi-Markov Jump Decision Using Multistep Transition and Mental Rehearsal

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Bingxuan Ren ◽  
Tangwen Yin ◽  
Shan Fu
Keyword(s):  
Decision Making ◽  
Transition Probability ◽  
Markov Property ◽  
Solution Space ◽  
Human Action ◽  
Flight Simulation ◽  
Impact Factors ◽  
Time Dimension ◽  
Mental Rehearsal ◽  
Markov Jump

Cognitive searching optimization is a subconscious mental phenomenon in decision making. Aroused by exploiting accessible human action, alleviating inefficient decision and shrinking searching space remain challenges for optimizing the solution space. Multiple decision estimation and the jumpy decision transition interval are two of the cross-impact factors resulting in variation of decision paths. To optimize the searching process of decision solution space, we propose a semi-Markov jump cognitive decision method in which a searching contraction index bridges correlation from the time dimension and depth dimension. With the change state and transition interval, the semi-Markov property can obtain the action by limiting the decision solution to the specified range. From the decision depth, bootstrap re-sampling utilizes mental rehearsal iteration to update the transition probability. In addition, dynamical decision boundary by the interaction process limits the admissible decisions. Through the flight simulation, we show that proposed index and reward vary with the transition decision steps and mental rehearsal frequencies. In conclusion, this decision-making method integrates the multistep transition and mental rehearsal on semi-Markov jump decision process, opening a route to the multiple dimension optimization of cognitive interaction.

scholarly journals Dam Deformation Interpretation and Prediction Based on a Long Short-Term Memory Model Coupled with an Attention Mechanism

2021 ◽  
Vol 11 (14) ◽  
pp. 6625
Author(s):  
Yan Su ◽  
Kailiang Weng ◽  
Chuan Lin ◽  
Zeqin Chen
Keyword(s):  
Short Term Memory ◽  
Attention Mechanism ◽  
Impact Factors ◽  
Nonlinear Prediction ◽  
Time Dimension ◽  
Short Term ◽  
Deformation Prediction ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Dam Deformation

An accurate dam deformation prediction model is vital to a dam safety monitoring system, as it helps assess and manage dam risks. Most traditional dam deformation prediction algorithms ignore the interpretation and evaluation of variables and lack qualitative measures. This paper proposes a data processing framework that uses a long short-term memory (LSTM) model coupled with an attention mechanism to predict the deformation response of a dam structure. First, the random forest (RF) model is introduced to assess the relative importance of impact factors and screen input variables. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) method is used to identify and filter the equipment based abnormal values to reduce the random error in the measurements. Finally, the coupled model is used to focus on important factors in the time dimension in order to obtain more accurate nonlinear prediction results. The results of the case study show that, of all tested methods, the proposed coupled method performed best. In addition, it was found that temperature and water level both have significant impacts on dam deformation and can serve as reliable metrics for dam management.

Kenneth Fisher’s heuristics

2016 ◽  
Vol 8 (2) ◽  
pp. 130-148
Author(s):  
Carlo Massironi ◽  
Giusy Chesini
Keyword(s):  
Decision Making ◽  
Conceptual Analysis ◽  
Investment Decision ◽  
Real Life ◽  
Human Action ◽  
Theory Of Knowledge ◽  
Constructivist Theory ◽  
Individual Investors ◽  
Content Type ◽  
Radical Constructivist

Purpose The authors are interested in building descriptive – real life – models of successful investors’ investment reasoning and decision-making. Models designed to be useful for trying to replicate and evolve their reasoning and decision-making. The purpose of this paper, a case study, is to take the substantial material – on innovating the investing tools – published in four books (2006/2012, 2010, 2011, 2015) by a US stock investor named Kenneth Fisher (CEO of Fisher Investments, Woodside, California) and sketch Fisher’s investment innovating reasoning model. Design/methodology/approach To sketch Fisher’s investment innovating reasoning model, the authors used the Radical constructivist theory of knowledge, a framework for analyzing human action and reasoning called Symbolic interactionism and a qualitative analytic technique called Conceptual analysis. The authors have done qualitative research applied to the study of investment decision-making of a single professional investor. Findings In the paper, the authors analyzed and described the heuristics used by Fisher to build subsequent generations of investing tools (called by Fisher “Capital Markets Technology”) to try to make better forecasts to beat the stock market. The authors were interested in studying the evolutive dimensions of the tools to make forecasts of a successful investor: the “how to build it” and “how to evolve it” dimension. Originality/value The paper offers an account of Kenneth Fisher’s framework to reason the innovation of investing tools. The authors believe that this paper could be of interest to professional money managers and to all those who are involved in the study and development of the tools of investing. This work is also an example of the use of the Radical constructivist theory of knowledge, the Symbolic interactionist framework and the Conceptual analysis to build descriptive models of investment reasoning of individual investors, models designed to enable the reproduction/approximation of the conceptual operations of the investor.

Asynchronous quadratic control for constrained hidden markov jump linear systems with incomplete MTPM and MOCPM

2021 ◽  
Author(s):  
Jin Zhu ◽  
Kai Xia ◽  
Geir E Dullerud
Keyword(s):  
Linear Systems ◽  
Controller Design ◽  
Hidden Markov ◽  
Transition Probability ◽  
Original System ◽  
Transition Probability Matrix ◽  
Markov Jump ◽  
Jump Linear Systems ◽  
Weighting Matrix ◽  
Markov Jump Linear Systems

Abstract This paper investigates the quadratic optimal control problem for constrained Markov jump linear systems with incomplete mode transition probability matrix (MTPM). Considering original system mode is not accessible, observed mode is utilized for asynchronous controller design where mode observation conditional probability matrix (MOCPM), which characterizes the emission between original modes and observed modes is assumed to be partially known. An LMI optimization problem is formulated for such constrained hidden Markov jump linear systems with incomplete MTPM and MOCPM. Based on this, a feasible state-feedback controller can be designed with the application of free-connection weighting matrix method. The desired controller, dependent on observed mode, is an asynchronous one which can minimize the upper bound of quadratic cost and satisfy restrictions on system states and control variables. Furthermore, clustering observation where observed modes recast into several clusters, is explored for simplifying the computational complexity. Numerical examples are provided to illustrate the validity.

Novel Application of Artificial Intelligence with Potential to Transform Well Planning Workflows on the Norwegian Continental Shelf

2021 ◽  
Author(s):  
Jon Gustav Vabø ◽  
Evan Thomas Delaney ◽  
Tom Savel ◽  
Norbert Dolle
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Continental Shelf ◽  
Data Science ◽  
Search Algorithm ◽  
Solution Space ◽  
Good Decision ◽  
Trade Offs ◽  
Well Trajectory ◽  
Norwegian Continental Shelf

Abstract This paper describes the transformational application of Artificial Intelligence (AI) in Equinor's annual well planning and maturation process. Well planning is a complex decision-making process, like many other processes in the industry. There are thousands of choices, conflicting business drivers, lots of uncertainty, and hidden bias. These complexities all add up, which makes good decision making very hard. In this application, AI has been used for automated and unbiased evaluation of the full solution space, with the objective to optimize the selection of drilling campaigns while taking into account complex issues such as anti-collision with existing wells, drilling hazards and trade-offs between cost, value and risk. Designing drillable well trajectories involves a sequence of decisions, which makes the process very suitable for AI algorithms. Different solver architectures, or algorithms, can be used to play this game. This is similar to how companies such as Google-owned DeepMind develop customized solvers for games such as Go and StarCraft. The chosen method is a Tree Search algorithm with an evolutionary layer on top, providing a good balance in terms of performance (i.e., speed) vs. exploration capability (i.e., it looks "wide" in the option space). The algorithm has been deployed in a full stack web-based application that allows users to follow an end-2-end workflow: from defining well trajectory design rules and constraints to running the AI engine and evaluating results to the optimization of multi-well drilling campaigns based on risk, value and cost objectives. The full-size paper describes different Norwegian Continental Shelf (NCS) use cases of this AI assisted well trajectory planning. Results to-date indicate significant CAPEX savings potential and step-change improvements in decision speed (months to days) compared to routine manual workflows. There are very limited real transformative examples of Artificial Intelligence in multi- disciplinary workflows. This paper therefore gives a unique insight how a combination of data science, domain expertise and end user feedback can lead to powerful and transformative AI solutions – implemented at scale within an existing organization.

scholarly journals Nature value: the evolution of this concept

2012 ◽  
Vol 36 (1) ◽  
pp. 9-15
Author(s):  
Sabrina Soares da Silva ◽  
Ricardo Pereira Reis ◽  
Patrícia Aparecida Ferreira
Keyword(s):  
Decision Making ◽  
Natural Resources ◽  
Environmental Degradation ◽  
Economic Valuation ◽  
Human Action ◽  
Decision Making Process ◽  
Environment Management ◽  
Goods And Services ◽  
Current Scenario ◽  
The Impact

More attention has been paid to environmental matters in recent years, mainly due to the current scenario of accentuated environmental degradation. The economic valuation of nature goods can contribute to the decision-making process in environment management, generating a more comprehensive informational base. This paper aims to present, in a historic perspective, the different concepts attributed to nature goods and were related to the current predominant perspectives of nature analyses. For this purpose, this paper presents the different concepts attributed to value since the pre-classical period, when nature were viewed as inert and passive providers of goods and services, this view legitimized nature's exploration without concern over the preservation and conservation of nature. The capacity of nature to absorb the impact of human action appears to be reaching its limit, considering the irreversibility, the irreproducibility and the possibility of collapse. The appropriate method for valuing natural resources is not known, but more important than the method is to respect and incorporate the particular characteristics of the nature goods into this process. These characteristics must be valuated in order to arrive at a more consistence approach to nature value and promote sustainability.

Modern Financial Market Theory – A Critique Based on the Logic of Human Action

2021 ◽  
Vol 54 (3) ◽  
pp. 447-467
Author(s):  
Thorsten Polleit
Keyword(s):  
Decision Making ◽  
Financial Markets ◽  
Financial Market ◽  
Real World ◽  
Rational Expectation ◽  
Efficient Market Hypothesis ◽  
Human Action ◽  
Market Theory ◽  
Expectation Theory ◽  
Market Economics

The modern financial market theory (MFMT) – based on the efficient market hypothesis, rational expectation theory, and modern portfolio theory – has become the standard approach in financial market economics. In this article, the MFMT will be critically ­reviewed using the logic of human action (or: praxeology) as an epistemological meta­theory. It will be shown that the MFMT exhibits (praxeo-)logical deficiencies so that it cannot provide investors with well-founded decision-making support in real-world financial markets.

Human Action in the Loop

2015 ◽  
pp. 352-368
Author(s):  
Hartwig Steusloff ◽  
Michael Decker
Keyword(s):  
Decision Making ◽  
Human Action ◽  
Effective Control ◽  
System Component ◽  
Ethical Reflection ◽  
High Expectations ◽  
Human In The Loop ◽  
Human Decision ◽  
System Life ◽  
The One

Extremely complex systems like the smart grid or autonomous cars need to meet society's high expectations regarding their safe operation. The human designer and operator becomes a “system component” as soon as responsible decision making is needed. Tacit knowledge and other human properties are of crucial relevance for situation-dependent decisions. The uniform modeling of technical systems and humans will benefit from ethical reflection. In this chapter, we describe human action with technical means and ask, on the one hand, for a comprehensive multidisciplinary technology assessment in order to produce supporting knowledge and methods for technical and societal decision making. On the other hand—and here is the focus—we propose a system life cycle approach which integrates the human in the loop and argue that it can be worthwhile to describe humans in a technical way in order to implement human decision making by means of the use case method. Ethical reflection and even ethically based technical decision making can support the effective control of convergent technology systems.

scholarly journals A Simplified Predictive Control of Constrained Markov Jump System with Mixed Uncertainties

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yanyan Yin ◽  
Yanqing Liu ◽  
Hamid R. Karimi
Keyword(s):  
Predictive Control ◽  
Transition Probability ◽  
Dynamic Performance ◽  
Markov Jump ◽  
Markov Jump System ◽  
Predictive Controller ◽  
Model Predictive Control Algorithm ◽  
Simplified Algorithm ◽  
Jump Systems ◽  
Mixed Uncertainties

A simplified model predictive control algorithm is designed for discrete-time Markov jump systems with mixed uncertainties. The mixed uncertainties include model polytope uncertainty and partly unknown transition probability. The simplified algorithm involves finite steps. Firstly, in the previous steps, a simplified mode-dependent predictive controller is presented to drive the state to the neighbor area around the origin. Then the trajectory of states is driven as expected to the origin by the final-step mode-independent predictive controller. The computational burden is dramatically cut down and thus it costs less time but has the acceptable dynamic performance. Furthermore, the polyhedron invariant set is utilized to enlarge the initial feasible area. The numerical example is provided to illustrate the efficiency of the developed results.

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