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Journal articles on the topic 'Sequential decision processes'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Alagoz, Oguzhan, Heather Hsu, Andrew J. Schaefer, and Mark S. Roberts. "Markov Decision Processes: A Tool for Sequential Decision Making under Uncertainty." Medical Decision Making 30, no. 4 (2009): 474–83. http://dx.doi.org/10.1177/0272989x09353194.

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We provide a tutorial on the construction and evaluation of Markov decision processes (MDPs), which are powerful analytical tools used for sequential decision making under uncertainty that have been widely used in many industrial and manufacturing applications but are underutilized in medical decision making (MDM). We demonstrate the use of an MDP to solve a sequential clinical treatment problem under uncertainty. Markov decision processes generalize standard Markov models in that a decision process is embedded in the model and multiple decisions are made over time. Furthermore, they have sign

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Sobel, Matthew J., and Wei Wei. "Myopic Solutions of Homogeneous Sequential Decision Processes." Operations Research 58, no. 4-part-2 (2010): 1235–46. http://dx.doi.org/10.1287/opre.1090.0767.

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3

El Chamie, Mahmoud, Dylan Janak, and Behçet Açıkmeşe. "Markov decision processes with sequential sensor measurements." Automatica 103 (May 2019): 450–60. http://dx.doi.org/10.1016/j.automatica.2019.02.026.

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4

Feinberg, Eugene A. "On essential information in sequential decision processes." Mathematical Methods of Operations Research 62, no. 3 (2005): 399–410. http://dx.doi.org/10.1007/s00186-005-0035-3.

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5

Maruyama, Yukihiro. "Strong representation theorems for bitone sequential decision processes." Optimization Methods and Software 18, no. 4 (2003): 475–89. http://dx.doi.org/10.1080/1055678031000154707.

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6

Milani Fard, M., and J. Pineau. "Non-Deterministic Policies in Markovian Decision Processes." Journal of Artificial Intelligence Research 40 (January 5, 2011): 1–24. http://dx.doi.org/10.1613/jair.3175.

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Markovian processes have long been used to model stochastic environments. Reinforcement learning has emerged as a framework to solve sequential planning and decision-making problems in such environments. In recent years, attempts were made to apply methods from reinforcement learning to construct decision support systems for action selection in Markovian environments. Although conventional methods in reinforcement learning have proved to be useful in problems concerning sequential decision-making, they cannot be applied in their current form to decision support systems, such as those in medica

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Maruyama, Yukihiro. "SUPER-STRONG REPRESENTATION THEOREMS FOR NONDETERMINISTIC SEQUENTIAL DECISION PROCESSES." Journal of the Operations Research Society of Japan 60, no. 2 (2017): 136–55. http://dx.doi.org/10.15807/jorsj.60.136.

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8

Hantula, Donald A., and Charles R. Crowell. "Intermittent Reinforcement and Escalation Processes in Sequential Decision Making:." Journal of Organizational Behavior Management 14, no. 2 (1994): 7–36. http://dx.doi.org/10.1300/j075v14n02_03.

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9

Canbolat, Pelin G., and Uriel G. Rothblum. "(Approximate) iterated successive approximations algorithm for sequential decision processes." Annals of Operations Research 208, no. 1 (2012): 309–20. http://dx.doi.org/10.1007/s10479-012-1073-x.

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10

Mahadevan, Sridhar. "Representation Discovery in Sequential Decision Making." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 1718–21. http://dx.doi.org/10.1609/aaai.v24i1.7766.

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Automatically constructing novel representations of tasks from analysis of state spaces is a longstanding fundamental challenge in AI. I review recent progress on this problem for sequential decision making tasks modeled as Markov decision processes. Specifically, I discuss three classes of representation discovery problems: finding functional, state, and temporal abstractions. I describe solution techniques varying along several dimensions: diagonalization or dilation methods using approximate or exact transition models; reward-specific vs reward-invariant methods; global vs. local representa

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Ying, Ming-Sheng, Yuan Feng, and Sheng-Gang Ying. "Optimal Policies for Quantum Markov Decision Processes." International Journal of Automation and Computing 18, no. 3 (2021): 410–21. http://dx.doi.org/10.1007/s11633-021-1278-z.

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AbstractMarkov decision process (MDP) offers a general framework for modelling sequential decision making where outcomes are random. In particular, it serves as a mathematical framework for reinforcement learning. This paper introduces an extension of MDP, namely quantum MDP (qMDP), that can serve as a mathematical model of decision making about quantum systems. We develop dynamic programming algorithms for policy evaluation and finding optimal policies for qMDPs in the case of finite-horizon. The results obtained in this paper provide some useful mathematical tools for reinforcement learning

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12

PENG, Yanyan, and Xinwang LIU. "BIDDING DECISION IN LAND AUCTION USING PROSPECT THEORY." International Journal of Strategic Property Management 19, no. 2 (2015): 186–205. http://dx.doi.org/10.3846/1648715x.2015.1047914.

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Land auction is widely practiced in company and government decisions, especially in China. Bidders are always faced with two or more auctions in the period of a decision cycle. The outcome of the auction is under high risk. The bidder's risk attitude and preference will have a great influence on his/her bidding price. Prospect theory is currently the main descriptive theory of decision under risk. In this paper, we will consider the preferences of the decision-makers in land bidding decisions with the multi-attribute additive utility and reference point method in cumulative prospect theory. Th

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13

Messias, João, Matthijs Spaan, and Pedro Lima. "GSMDPs for Multi-Robot Sequential Decision-Making." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 1408–14. http://dx.doi.org/10.1609/aaai.v27i1.8550.

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Markov Decision Processes (MDPs) provide an extensive theoretical background for problems of decision-making under uncertainty. In order to maintain computational tractability, however, real-world problems are typically discretized in states and actions as well as in time. Assuming synchronous state transitions and actions at fixed rates may result in models which are not strictly Markovian, or where agents are forced to idle between actions, losing their ability to react to sudden changes in the environment. In this work, we explore the application of Generalized Semi-Markov Decision Processe

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14

Sin, Yeonju, HeeYoung Seon, Yun Kyoung Shin, Oh-Sang Kwon, and Dongil Chung. "Subjective optimality in finite sequential decision-making." PLOS Computational Biology 17, no. 12 (2021): e1009633. http://dx.doi.org/10.1371/journal.pcbi.1009633.

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Many decisions in life are sequential and constrained by a time window. Although mathematically derived optimal solutions exist, it has been reported that humans often deviate from making optimal choices. Here, we used a secretary problem, a classic example of finite sequential decision-making, and investigated the mechanisms underlying individuals’ suboptimal choices. Across three independent experiments, we found that a dynamic programming model comprising subjective value function explains individuals’ deviations from optimality and predicts the choice behaviors under fewer and more opportu

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Mármol, Amparo M., Justo Puerto, and Francisco R. Fernández. "Sequential incorporation of imprecise information in multiple criteria decision processes." European Journal of Operational Research 137, no. 1 (2002): 123–33. http://dx.doi.org/10.1016/s0377-2217(01)00082-0.

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16

Khader, Patrick H., Thorsten Pachur, Lilian A. E. Weber, and Kerstin Jost. "Neural Signatures of Controlled and Automatic Retrieval Processes in Memory-based Decision-making." Journal of Cognitive Neuroscience 28, no. 1 (2016): 69–83. http://dx.doi.org/10.1162/jocn_a_00882.

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Decision-making often requires retrieval from memory. Drawing on the neural ACT-R theory [Anderson, J. R., Fincham, J. M., Qin, Y., & Stocco, A. A central circuit of the mind. Trends in Cognitive Sciences, 12, 136–143, 2008] and other neural models of memory, we delineated the neural signatures of two fundamental retrieval aspects during decision-making: automatic and controlled activation of memory representations. To disentangle these processes, we combined a paradigm developed to examine neural correlates of selective and sequential memory retrieval in decision-making with a manipulatio

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Chen, Haiyang, Hyung Jin Chang, and Andrew Howes. "Apparently Irrational Choice as Optimal Sequential Decision Making." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (2021): 792–800. http://dx.doi.org/10.1609/aaai.v35i1.16161.

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In this paper, we propose a normative approach to modeling apparently human irrational decision making (cognitive biases) that makes use of inherently rational computational mechanisms. We view preferential choice tasks as sequential decision making problems and formulate them as Partially Observable Markov Decision Processes (POMDPs). The resulting sequential decision model learns what information to gather about which options, whether to calculate option values or make comparisons between options and when to make a choice. We apply the model to choice problems where context is known to influ

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Khan, Omar, Pascal Poupart, and James Black. "Minimal Sufficient Explanations for Factored Markov Decision Processes." Proceedings of the International Conference on Automated Planning and Scheduling 19 (October 16, 2009): 194–200. http://dx.doi.org/10.1609/icaps.v19i1.13365.

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Explaining policies of Markov Decision Processes (MDPs) is complicated due to their probabilistic and sequential nature. We present a technique to explain policies for factored MDP by populating a set of domain-independent templates. We also present a mechanism to determine a minimal set of templates that, viewed together, completely justify the policy. Our explanations can be generated automatically at run-time with no additional effort required from the MDP designer. We demonstrate our technique using the problems of advising undergraduate students in their course selection and assisting peo

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19

Farina, Gabriele, Christian Kroer, and Tuomas Sandholm. "Online Convex Optimization for Sequential Decision Processes and Extensive-Form Games." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1917–25. http://dx.doi.org/10.1609/aaai.v33i01.33011917.

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Regret minimization is a powerful tool for solving large-scale extensive-form games. State-of-the-art methods rely on minimizing regret locally at each decision point. In this work we derive a new framework for regret minimization on sequential decision problems and extensive-form games with general compact convex sets at each decision point and general convex losses, as opposed to prior work which has been for simplex decision points and linear losses. We call our framework laminar regret decomposition. It generalizes the CFR algorithm to this more general setting. Furthermore, our framework

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20

Oh-Hyun Jung. "Are Sequential Decision-Making Processes of Tourists and Consumers the Same?" Culinary Science & Hospitality Research 23, no. 6 (2017): 161–72. http://dx.doi.org/10.20878/cshr.2017.23.6.018.

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21

Chang, H. S. "A Model for Multi-timescaled Sequential Decision-making Processes with Adversary." Mathematical and Computer Modelling of Dynamical Systems 10, no. 3-4 (2004): 287–302. http://dx.doi.org/10.1080/13873950412331335261.

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22

Kneller, Wendy, Amina Memon, and Sarah Stevenage. "Simultaneous and sequential lineups: decision processes of accurate and inaccurate eyewitnesses." Applied Cognitive Psychology 15, no. 6 (2001): 659–71. http://dx.doi.org/10.1002/acp.739.

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23

Jean, Mbihi, Owoundi Etouké Paul, and Biyobo Obono Arnaud. "Matlab-Based Modelling and Dynamic Optimization of a Class of Sequential Decision Processes." European Journal of Advances in Engineering and Technology 9, no. 12 (2022): 90–100. https://doi.org/10.5281/zenodo.10647148.

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<strong>ABSTRACT</strong> In this paper, the discrete models of two types of sequential decision processes (i.e. open and closed graph topologies) are developed. Under the adopted counting policy of nodes, it is shown that a sequential open graph topology with n levels along  the x-axis,  involves a total of  n (n+1)/2 states (nodes). However, for a closed graph topology with 2n-1 levels along  the x-axis,  it is shown also that the total number of state (nodes) is n2. In addition, for both types of open and closed graph processes,  their dynamic state models are

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24

Becker, R., S. Zilberstein, V. Lesser, and C. V. Goldman. "Solving Transition Independent Decentralized Markov Decision Processes." Journal of Artificial Intelligence Research 22 (December 1, 2004): 423–55. http://dx.doi.org/10.1613/jair.1497.

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Formal treatment of collaborative multi-agent systems has been lagging behind the rapid progress in sequential decision making by individual agents. Recent work in the area of decentralized Markov Decision Processes (MDPs) has contributed to closing this gap, but the computational complexity of these models remains a serious obstacle. To overcome this complexity barrier, we identify a specific class of decentralized MDPs in which the agents' transitions are independent. The class consists of independent collaborating agents that are tied together through a structured global reward function tha

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25

Tump, Alan N., Timothy J. Pleskac, and Ralf H. J. M. Kurvers. "Wise or mad crowds? The cognitive mechanisms underlying information cascades." Science Advances 6, no. 29 (2020): eabb0266. http://dx.doi.org/10.1126/sciadv.abb0266.

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Whether getting vaccinated, buying stocks, or crossing streets, people rarely make decisions alone. Rather, multiple people decide sequentially, setting the stage for information cascades whereby early-deciding individuals can influence others’ choices. To understand how information cascades through social systems, it is essential to capture the dynamics of the decision-making process. We introduce the social drift–diffusion model to capture these dynamics. We tested our model using a sequential choice task. The model was able to recover the dynamics of the social decision-making process, accu

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Ortega-Gutiérrez, R. Israel, and H. Cruz-Suárez. "A Moreau-Yosida regularization for Markov decision processes." Proyecciones (Antofagasta) 40, no. 1 (2020): 117–37. http://dx.doi.org/10.22199/issn.0717-6279-2021-01-0008.

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This paper addresses a class of sequential optimization problems known as Markov decision processes. These kinds of processes are considered on Euclidean state and action spaces with the total expected discounted cost as the objective function. The main goal of the paper is to provide conditions to guarantee an adequate Moreau-Yosida regularization for Markov decision processes (named the original process). In this way, a new Markov decision process that conforms to the Markov control model of the original process except for the cost function induced via the Moreau-Yosida regularization is est

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27

Ortega-Gutiérrez, R. Israel, and H. Cruz-Suárez. "A Moreau-Yosida regularization for Markov decision processes." Proyecciones (Antofagasta) 40, no. 1 (2020): 117–37. http://dx.doi.org/10.22199/issn.0717-6279-2021-01-0008.

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This paper addresses a class of sequential optimization problems known as Markov decision processes. These kinds of processes are considered on Euclidean state and action spaces with the total expected discounted cost as the objective function. The main goal of the paper is to provide conditions to guarantee an adequate Moreau-Yosida regularization for Markov decision processes (named the original process). In this way, a new Markov decision process that conforms to the Markov control model of the original process except for the cost function induced via the Moreau-Yosida regularization is est

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28

Scherbaum, Stefan, Simon Frisch, Susanne Leiberg, Steven J. Lade, Thomas Goschke, and Maja Dshemuchadse. "Process dynamics in delay discounting decisions: An attractor dynamics approach." Judgment and Decision Making 11, no. 5 (2016): 472–95. http://dx.doi.org/10.1017/s1930297500004575.

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AbstractHow do people make decisions between an immediate but small reward and a delayed but large one? The outcome of such decisions indicates that people discount rewards by their delay and hence these outcomes are well described by discounting functions. However, to understand irregular decisions and dysfunctional behavior one needs models which describe how the process of making the decision unfolds dynamically over time: how do we reach a decision and how do sequential decisions influence one another? Here, we present an attractor model that integrates into and extends discounting functio

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Oscarsson, Henrik, and Maria Oskarson. "Sequential vote choice: Applying a consideration set model of heterogeneous decision processes." Electoral Studies 57 (February 2019): 275–83. http://dx.doi.org/10.1016/j.electstud.2018.08.005.

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30

Varona, Pablo, and Mikhail I. Rabinovich. "Hierarchical dynamics of informational patterns and decision-making." Proceedings of the Royal Society B: Biological Sciences 283, no. 1832 (2016): 20160475. http://dx.doi.org/10.1098/rspb.2016.0475.

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Traditional studies on the interaction of cognitive functions in healthy and disordered brains have used the analyses of the connectivity of several specialized brain networks—the functional connectome. However, emerging evidence suggests that both brain networks and functional spontaneous brain-wide network communication are intrinsically dynamic. In the light of studies investigating the cooperation between different cognitive functions, we consider here the dynamics of hierarchical networks in cognitive space. We show, using an example of behavioural decision-making based on sequential epis

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31

Tan, Chin Hon, and Joseph C. Hartman. "Sensitivity Analysis in Markov Decision Processes with Uncertain Reward Parameters." Journal of Applied Probability 48, no. 4 (2011): 954–67. http://dx.doi.org/10.1239/jap/1324046012.

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Sequential decision problems can often be modeled as Markov decision processes. Classical solution approaches assume that the parameters of the model are known. However, model parameters are usually estimated and uncertain in practice. As a result, managers are often interested in how estimation errors affect the optimal solution. In this paper we illustrate how sensitivity analysis can be performed directly for a Markov decision process with uncertain reward parameters using the Bellman equations. In particular, we consider problems involving (i) a single stationary parameter, (ii) multiple s

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Tan, Chin Hon, and Joseph C. Hartman. "Sensitivity Analysis in Markov Decision Processes with Uncertain Reward Parameters." Journal of Applied Probability 48, no. 04 (2011): 954–67. http://dx.doi.org/10.1017/s002190020000855x.

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Sequential decision problems can often be modeled as Markov decision processes. Classical solution approaches assume that the parameters of the model are known. However, model parameters are usually estimated and uncertain in practice. As a result, managers are often interested in how estimation errors affect the optimal solution. In this paper we illustrate how sensitivity analysis can be performed directly for a Markov decision process with uncertain reward parameters using the Bellman equations. In particular, we consider problems involving (i) a single stationary parameter, (ii) multiple s

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33

Chatterjee, Krishnendu, Martin Chmelík, Deep Karkhanis, Petr Novotný, and Amélie Royer. "Multiple-Environment Markov Decision Processes: Efficient Analysis and Applications." Proceedings of the International Conference on Automated Planning and Scheduling 30 (June 1, 2020): 48–56. http://dx.doi.org/10.1609/icaps.v30i1.6644.

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Multiple-environment Markov decision processes (MEMDPs) are MDPs equipped with not one, but multiple probabilistic transition functions, which represent the various possible unknown environments. While the previous research on MEMDPs focused on theoretical properties for long-run average payoff, we study them with discounted-sum payoff and focus on their practical advantages and applications. MEMDPs can be viewed as a special case of Partially observable and Mixed observability MDPs: the state of the system is perfectly observable, but not the environment. We show that the specific structure o

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34

Meggendorfer, Tobias, Maximilian Weininger, and Patrick Wienhöft. "Solving Robust Markov Decision Processes: Generic, Reliable, Efficient." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 25 (2025): 26631–41. https://doi.org/10.1609/aaai.v39i25.34865.

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Markov decision processes (MDP) are a well-established model for sequential decision-making in the presence of probabilities. In *robust* MDP (RMDP), every action is associated with an *uncertainty set* of probability distributions, modelling that transition probabilities are not known precisely. Based on the known theoretical connection to stochastic games, we provide a framework for solving RMDPs that is generic, reliable, and efficient. It is *generic* both with respect to the model, allowing for a wide range of uncertainty sets, including but not limited to intervals, L1- or L2-balls, and

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35

Cao, Longbing, and Chengzhang Zhu. "Personalized next-best action recommendation with multi-party interaction learning for automated decision-making." PLOS ONE 17, no. 1 (2022): e0263010. http://dx.doi.org/10.1371/journal.pone.0263010.

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Automated next-best action recommendation for each customer in a sequential, dynamic and interactive context has been widely needed in natural, social and business decision-making. Personalized next-best action recommendation must involve past, current and future customer demographics and circumstances (states) and behaviors, long-range sequential interactions between customers and decision-makers, multi-sequence interactions between states, behaviors and actions, and their reactions to their counterpart’s actions. No existing modeling theories and tools, including Markovian decision processes

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Perrault, Andrew. "Monitoring and Intervening on Large Populations of Weakly Coupled Processes with Social Impact Applications." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 15450. http://dx.doi.org/10.1609/aaai.v37i13.26817.

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Many real-world sequential decision problems can be decomposed into processes with independent dynamics that are coupled via the action structure. We discuss recent work on such problems and future directions.

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Takayama, Shota, and Katsuhide Fujita. "Sequential Order Adjustment of Action Decisions for Multi-Agent Transformer (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 28 (2025): 29509–11. https://doi.org/10.1609/aaai.v39i28.35306.

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Multi-agent reinforcement learning (MARL) trains multiple agents in shared environments. Recently, MARL models have significantly improved performance by leveraging sequential decision-making processes. Although these models can enhance performance, they do not explicitly con-sider the importance of the order in which agents make decisions. We propose AOAD-MAT, a novel model incorporating action decision sequence into learning. AOAD-MAT uses a Transformer-based actor-critic architecture to dynamically adjust agent action order. It introduces a subtask predicting the next agent to act, integrat

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Carr, Steven, Nils Jansen, and Ufuk Topcu. "Task-Aware Verifiable RNN-Based Policies for Partially Observable Markov Decision Processes." Journal of Artificial Intelligence Research 72 (November 18, 2021): 819–47. http://dx.doi.org/10.1613/jair.1.12963.

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Partially observable Markov decision processes (POMDPs) are models for sequential decision-making under uncertainty and incomplete information. Machine learning methods typically train recurrent neural networks (RNN) as effective representations of POMDP policies that can efficiently process sequential data. However, it is hard to verify whether the POMDP driven by such RNN-based policies satisfies safety constraints, for instance, given by temporal logic specifications. We propose a novel method that combines techniques from machine learning with the field of formal methods: training an RNN-b

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Chen, Richard C., Kevin Wagner, and Gilmer L. Blankenship. "Constrained Partially Observed Markov Decision Processes With Probabilistic Criteria for Adaptive Sequential Detection." IEEE Transactions on Automatic Control 58, no. 2 (2013): 487–93. http://dx.doi.org/10.1109/tac.2012.2208312.

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Tkatek, Said, Saadia Bahti, Otman Abdoun, and Jaafar Abouchabaka. "Intelligent system for recruitment decision making using an alternative parallel-sequential genetic algorithm." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 1 (2021): 385–95. https://doi.org/10.11591/ijeecs.v22.i1.pp385-395.

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The human resources (HR) manager needs effective tools to be able to move away from traditional recruitment processes to make the good decision to select the good candidates for the good posts. To do this, we deliver an intelligent recruitment decision-making method for HR, incorporating a recruitment model based on the multipack model known as the NP-hard model. The system, which is a decision support tool, often integrates a genetic approach that operates alternately in parallel and sequentially. This approach will provide the best recruiting solution to allow HR managers to make the right d

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Chowdhury, Sayak Ray, and Xingyu Zhou. "Differentially Private Regret Minimization in Episodic Markov Decision Processes." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6375–83. http://dx.doi.org/10.1609/aaai.v36i6.20588.

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We study regret minimization in finite horizon tabular Markov decision processes (MDPs) under the constraints of differential privacy (DP). This is motivated by the widespread applications of reinforcement learning (RL) in real-world sequential decision making problems, where protecting users' sensitive and private information is becoming paramount. We consider two variants of DP -- joint DP (JDP), where a centralized agent is responsible for protecting users' sensitive data and local DP (LDP), where information needs to be protected directly on the user side. We first propose two general fram

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Fejgin, Naomi, and Ronit Hanegby. "Physical Educators’ Participation in Decision-Making Processes in Dynamic Schools." Journal of Teaching in Physical Education 18, no. 2 (1999): 141–58. http://dx.doi.org/10.1123/jtpe.18.2.141.

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Teacher participation in school decision-making processes is considered one of the major components of school dynamics. It is not known, however, whether all teachers participate in the process to the same extent. This study examines whether teacher participation is related to school dynamics and to subject matter taught. In a 3-step sequential model, the relative contribution of background variables, school measures, school dynamics, and subject matter taught to teacher participation was estimated. Findings showed that school dynamics had the strongest effect on teacher participation, but the

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Schrift, Rom Y., Jeffrey R. Parker, Gal Zauberman, and Shalena Srna. "Multistage Decision Processes: The Impact of Attribute Order on How Consumers Mentally Represent Their Choice." Journal of Consumer Research 44, no. 6 (2017): 1307–24. http://dx.doi.org/10.1093/jcr/ucx099.

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Abstract With the ever-increasing number of options from which consumers can choose, many decisions are made in stages. Whether using decision tools to sort, screen, and eliminate options, or intuitively trying to reduce the complexity of a choice, consumers often reach a decision by making sequential, attribute-level choices. The current article explores how the order in which attribute-level choices are made in such multistage decisions affects how consumers mentally represent and categorize their chosen option. The authors find that attribute choices made in the initial stage play a dominan

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Farina, Gabriele, Robin Schmucker, and Tuomas Sandholm. "Bandit Linear Optimization for Sequential Decision Making and Extensive-Form Games." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (2021): 5372–80. http://dx.doi.org/10.1609/aaai.v35i6.16677.

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Tree-form sequential decision making (TFSDM) extends classical one-shot decision making by modeling tree-form interactions between an agent and a potentially adversarial environment. It captures the online decision-making problems that each player faces in an extensive-form game, as well as Markov decision processes and partially-observable Markov decision processes where the agent conditions on observed history. Over the past decade, there has been considerable effort into designing online optimization methods for TFSDM. Virtually all of that work has been in the full-feedback setting, where

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Söllner, Anke, Arndt Bröder, and Benjamin E. Hilbig. "Deliberation versus automaticity in decision making: Which presentation format features facilitate automatic decision making?" Judgment and Decision Making 8, no. 3 (2013): 278–98. http://dx.doi.org/10.1017/s1930297500005982.

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AbstractThe idea of automatic decision making approximating normatively optimal decisions without necessitating much cognitive effort is intriguing. Whereas recent findings support the notion that such fast, automatic processes explain empirical data well, little is known about the conditions under which such processes are selected rather than more deliberate stepwise strategies. We investigate the role of the format of information presentation, focusing explicitly on the ease of information acquisition and its influence on information integration processes. In a probabilistic inference task,

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Kasianova, Ksenia, and Mark Kelbert. "Context-Dependent Criteria for Dirichlet Process in Sequential Decision-Making Problems." Mathematics 12, no. 21 (2024): 3321. http://dx.doi.org/10.3390/math12213321.

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In models with insufficient initial information, parameter estimation can be subject to statistical uncertainty, potentially resulting in suboptimal decision-making; however, delaying implementation to gather more information can also incur costs. This paper examines an extension of information-theoretic approaches designed to address this classical dilemma, focusing on balancing the expected profits and the information needed to be obtained about all of the possible outcomes. Initially utilized in binary outcome scenarios, these methods leverage information measures to harmonize competing obj

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Tkatek, Said, Saadia Bahti, Otman Abdoun, and Jaafar Abouchabaka. "Intelligent system for recruitment decision making using an alternative parallel-sequential genetic algorithm." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 1 (2021): 385. http://dx.doi.org/10.11591/ijeecs.v22.i1.pp385-395.

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<p>The human resources (HR) manager needs effective tools to be able to move away from traditional recruitment processes to make the good decision to select the good candidates for the good posts. To do this, we deliver an intelligent recruitment decision-making method for HR, incorporating a recruitment model based on the multipack model known as the NP-hard model. The system, which is a decision support tool, often integrates a genetic approach that operates alternately in parallel and sequentially. This approach will provide the best recruiting solution to allow HR managers to make th

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Fontanesi, Laura, Amitai Shenhav, and Sebastian Gluth. "Disentangling choice value and choice conflict in sequential decisions under risk." PLOS Computational Biology 18, no. 10 (2022): e1010478. http://dx.doi.org/10.1371/journal.pcbi.1010478.

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Recent years have witnessed a surge of interest in understanding the neural and cognitive dynamics that drive sequential decision making in general and foraging behavior in particular. Due to the intrinsic properties of most sequential decision-making paradigms, however, previous research in this area has suffered from the difficulty to disentangle properties of the decision related to (a) the value of switching to a new patch versus, which increases monotonically, and (b) the conflict experienced between choosing to stay or leave, which first increases but then decreases after reaching the po

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She, Chung, and Han. "Economic and Environmental Optimization of the Forest Supply Chain for Timber and Bioenergy Production from Beetle-Killed Forests in Northern Colorado." Forests 10, no. 8 (2019): 689. http://dx.doi.org/10.3390/f10080689.

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Harvesting mountain pine beetle-infested forest stands in the northern Colorado Rocky Mountains provides an opportunity to utilize otherwise wasted resources, generate net revenues, and minimize greenhouse gas (GHG) emissions. Timber and bioenergy production are commonly managed separately, and their integration is seldom considered. Yet, degraded wood and logging residues can provide a feedstock for bioenergy, while the sound wood from beetle-killed stands can still be used for traditional timber products. In addition, beneficial greenhouse gas emission (GHG) savings are often realized only b

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Brázdil, Tomáš, Krishnendu Chatterjee, Petr Novotný, and Jiří Vahala. "Reinforcement Learning of Risk-Constrained Policies in Markov Decision Processes." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 06 (2020): 9794–801. http://dx.doi.org/10.1609/aaai.v34i06.6531.

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Markov decision processes (MDPs) are the defacto framework for sequential decision making in the presence of stochastic uncertainty. A classical optimization criterion for MDPs is to maximize the expected discounted-sum payoff, which ignores low probability catastrophic events with highly negative impact on the system. On the other hand, risk-averse policies require the probability of undesirable events to be below a given threshold, but they do not account for optimization of the expected payoff. We consider MDPs with discounted-sum payoff with failure states which represent catastrophic outc

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