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A Survey on: Stratified mapping of Microarray Gene Expression datasets to decision tree algorithm aided through Evolutionary Design
2014
IOSR Journal of Computer Engineering
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Conceptual simple decision making models with the possibility of automatic learning are the most appropriate for performing such tasks. ...
In medical decision making (classification, diagnosing) there are many situations where decision must be made effectively and reliably. ...
We thank the anonymous reviewers for their comments. ...
doi:10.9790/0661-16650106
fatcat:4iatqoipf5edblrtwmr6mclxoa
Inductive Policy Selection for First-Order MDPs
[article]
2012
arXiv
pre-print
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We find policies that generalize well as the number of objects in the domain grows, potentially without bound. ...
We select policies for large Markov Decision Processes (MDPs) with compact first-order representations. ...
Two factors often make it unnatural to capture a planning domain in PSTRIPS. ...
arXiv:1301.0614v1
fatcat:c3mzfnucwzcxfdr7e6vfv2oai4
Learning-Assisted Automated Planning: Looking Back, Taking Stock, Going Forward
2003
The AI Magazine
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We extend the survey analysis to suggest promising avenues for future research in learning based on both previous experience and current needs in the planning community. ...
Possible target functions for inductively learning or improving heuristics include term weights that are most likely to lead to higher-quality solutions for a given domain, term weights that will be most ...
a search heuristic based on the problem or domain. ...
doi:10.1609/aimag.v24i2.1705
dblp:journals/aim/ZimmermanK03
fatcat:5umdmeki4zdlznah2hhdrjpe5e
Sub-optimal reasons for rejecting optimality
2000
Behavioral and Brain Sciences
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: In many situations, experts can achieve near-optimal performance. ...
The models Gigerenzer et al. evaluate fail to account for many of the most robust properties of human decision making, including examples of optimality. ...
s argument for the role of fast and frugal algorithms in human decision-making (Gigerenzer et al. 1999) . ...
doi:10.1017/s0140525x00453446
fatcat:3c5fqcq6aje5refucudt74pxsq
Semi-Supervised Learning with Declaratively Specified Entropy Constraints
[article]
2018
arXiv
pre-print
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such as co-training and novel domain-specific heuristics. ...
In addition to representing individual SSL heuristics, we show that multiple heuristics can also be automatically combined using Bayesian optimization methods. ...
If we assume that a hyperlink from document x 1 to x 2 is indicated by the fact near(x1,x2) then we can encourage a classifier to make similar decisions for the neighbors of a document with the neighbor ...
arXiv:1804.09238v2
fatcat:prq6iil3bbhbfgawys2uvyxeqq
Rules of Thumb in Life-cycle Saving Decisions*
2012
Economic Journal
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We analyse life-cycle saving decisions when households use simple heuristics, or rules of thumb, rather than solve the underlying intertemporal optimization problem. ...
that do not require computationally demanding tasks such as backward induction. , the special issue editor (Rachel Griffith) and referees, and participants at numerous seminars and conferences for helpful ...
It is a well-known finding from psychological research on decision-making that individuals use heuristics, or rules of thumb, in making judgements and decisions. ...
doi:10.1111/j.1468-0297.2012.02502.x
fatcat:ku2hniv7t5h75d6v6cjrueufta
Idealizations of Uncertainty, and Lessons from Artificial Intelligence
2016
Economics : the Open-Access, Open-Assessment e-Journal
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Under the radically uncertain conditions, human decision-making (for all its problems) has proved relatively robust, while decision making relying solely on deterministic rules or probabilistic models ...
This paper will argue that while prescriptive AI systems have been created that are effective for many engineered domains as aids for human decision makers, successes of these uses of AI should not obscure ...
In fact, Gigerenzer and Brighton show such heuristics at play as real human decision-making processes. ...
doi:10.5018/economics-ejournal.ja.2016-7
fatcat:2cdxboiifvaozjqo7zt5hog63a
Inducing decision trees with an ant colony optimization algorithm
2012
Applied Soft Computing
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In this paper we propose a novel ACO algorithm to induce decision trees, combining commonly used strategies from both traditional decision tree induction algorithms and ACO. ...
While ant colony optimization (ACO) algorithms have been successfully applied to extract classification rules, decision tree induction with ACO algorithms remains an almost unexplored research area. ...
Acknowledgement We thank Jan Kozak for providing a binary version of the cACDT algorithm and for his help in the preparation of the cACDT's data sets. ...
doi:10.1016/j.asoc.2012.05.028
fatcat:3wyxkn2kgrb5zaiuxait2icvxi
Decision tree design using information theory
1990
Knowledge Acquistion
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In this paper we examine the greedy mutual information algorithm for decision tree I design, analysing both theoretical and practical applications (namely, edge detec-.;tion). ...
An application to edge detection is described where we primarily emphasize the inductive methodology rather than the domain application (image processing) per se. ...
A recursive partitioning decision rule for non-parametric class-A. P. (1985). PREDICTOR: an alternative approach to uncertain inference in expert systems. ...
doi:10.1016/s1042-8143(05)80020-2
fatcat:blspffymfrch3feu2vpopkmuia
Solving stochastic resource-constrained project scheduling problems by closed-loop approximate dynamic programming
2015
European Journal of Operational Research
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To enhance performance of the rollout algorithm, we employ constraint programming (CP) to improve the performance of base policy offered by a priority-rule heuristic. ...
In this research, we develop effective and efficient approximate dynamic programming (ADP) algorithms based on the rollout policy for this category of stochastic scheduling problems. ...
Salim Rostami for kindly providing their PPGA code for our computational study. We also thank three anonymous referees for their ...
doi:10.1016/j.ejor.2015.04.015
fatcat:lwyoy2pzsfhbplhu4aczvrpzka
Learning Hierarchical Skills from Observation
[chapter]
2002
Lecture Notes in Computer Science
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The resulting program is concise and easy to understand, making it possible to view program induction as a practical technique for knowledge acquisition. ...
We infer these programs using a three-stage process that learns flat unordered rules, combines these rules into a classification hierarchy, and finally translates this structure into a hierarchical reactive ...
Thus, our heuristics for rule combination regularized the representation. ...
doi:10.1007/3-540-36182-0_22
fatcat:7ru4gk3zd5arndgte3ng4eza74
Characterising bias in regulatory risk and decision analysis: An analysis of heuristics applied in health technology appraisal, chemicals regulation, and climate change governance
2017
Environment International
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Heuristic decision rules (e.g. feasibility rules) in principle act as surrogates for utility maximisation or distributional concerns, yet in practice may neglect costs and benefits, be based on arbitrary ...
A B S T R A C T In many environmental and public health domains, heuristic methods of risk and decision analysis must be relied upon, either because problem structures are ambiguous, reliable data is lacking ...
And whilst we find similar heuristics for extrapolation and interpolation across these domains, there are differences in the degree to which different jurisdictions and policy domains are bound by rules ...
doi:10.1016/j.envint.2017.05.002
pmid:28499120
fatcat:ddjp4jt5ebcixjfnq6ivpnm4u4
Optimally Solving Dec-POMDPs as Continuous-State MDPs
2016
The Journal of Artificial Intelligence Research
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Decentralized partially observable Markov decision processes (Dec-POMDPs) provide a general model for decision-making under uncertainty in decentralized settings, but are difficult to solve optimally ( ...
To provide scalability, we refine this approach by combining heuristic search and compact representations that exploit the structure present in multi-agent domains, without losing the ability to converge ...
Experimentally, we show that FB-HSVI is able to outperform all current state-of-the-art exact Dec-POMDP solvers in common benchmark domains. ...
doi:10.1613/jair.4623
fatcat:bha4xomrwjbphbutotnrcyciqa
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies
[article]
2020
arXiv
pre-print
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Our work provides theoretical, as well as practical, foundations for clinician/human-in-the-loop decision making, in which humans (e.g., clinicians, patients) can incorporate additional knowledge (e.g. ...
We propose a model-free algorithm based on temporal difference learning and a near-greedy heuristic for action selection. ...
The authors would like to thank Satinder Singh, Brahmajee Nallamothu, Jessica Golbus, and members of the MLD3 group for helpful discussions regarding this work, as well as the reviewers for constructive ...
arXiv:2007.12678v1
fatcat:a4k5hgwhyre5zbgqgpy4pddpwe
Sequential decision models for expert system optimization
1997
IEEE Transactions on Knowledge and Data Engineering
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For model formulation, we classify sequential decision models by objective (cost minimization versus value maximization) knowledge source (rules, data, belief network, etc.), and optimized form (decision ...
For solution techniques, we demonstrate how search methods and heuristics are influenced by economic objective, knowledge source, and optimized form. ...
The loss criterion heuristic, proposed in [DM93], also produces near-optimal solutions. The loss criterion heuristic is similar to the expected rule heuristic. ...
doi:10.1109/69.634747
fatcat:chvlqultkrcqfggehcr2ga6fve
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