scholarly journals Inferring Agents’ Goals from Observing Successful Traces

2021 ◽  
Vol 11 (9) ◽  
pp. 4116
Author(s):  
Guillaume Lorthioir ◽  
Katsumi Inoue ◽  
Gauvain Bourgne
Keyword(s):  
Concept Learning ◽  
Propositional Logic ◽  
Learning Algorithm ◽  
Search Space ◽  
Plan Recognition ◽  
Goal Recognition

Goal recognition is a sub-field of plan recognition that focuses on the goals of an agent. Current approaches in goal recognition have not yet tried to apply concept learning to a propositional logic formalism. In this paper, we extend our method for inferring an agent’s possible goal by observing this agent in a series of successful attempts to reach its goal and using concept learning on these observations. We propose an algorithm, LFST (Learning From Successful Traces), to produce concise hypotheses about the agent’s goal. We show that if such a goal exists, our algorithm always provides a possible goal for the agent, and we evaluate the performance of our algorithm in different settings. We compare it to another concept-learning algorithm that uses a formalism close to ours, and we obtain better results at producing the hypotheses with our algorithm. We introduce a way to use assumptions about the agent’s behavior and the dynamics of the environment, thus improving the agent’s goal deduction by optimizing the potential goals’ search space.

Dementia key gene identification with multi-layered SNP-gene-disease network

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i831-i839
Author(s):  
Dong-gi Lee ◽  
Myungjun Kim ◽  
Sang Joon Son ◽  
Chang Hyung Hong ◽  
Hyunjung Shin
Keyword(s):  
Candidate Genes ◽  
Learning Algorithm ◽  
Search Space ◽  
Supplementary Information ◽  
Gene Identification ◽  
Nucleotide Polymorphisms ◽  
Disease Network ◽  
Single Nucleotide ◽  
Key Genes ◽  
Significant Attention

Abstract Motivation Recently, various approaches for diagnosing and treating dementia have received significant attention, especially in identifying key genes that are crucial for dementia. If the mutations of such key genes could be tracked, it would be possible to predict the time of onset of dementia and significantly aid in developing drugs to treat dementia. However, gene finding involves tremendous cost, time and effort. To alleviate these problems, research on utilizing computational biology to decrease the search space of candidate genes is actively conducted. In this study, we propose a framework in which diseases, genes and single-nucleotide polymorphisms are represented by a layered network, and key genes are predicted by a machine learning algorithm. The algorithm utilizes a network-based semi-supervised learning model that can be applied to layered data structures. Results The proposed method was applied to a dataset extracted from public databases related to diseases and genes with data collected from 186 patients. A portion of key genes obtained using the proposed method was verified in silico through PubMed literature, and the remaining genes were left as possible candidate genes. Availability and implementation The code for the framework will be available at http://www.alphaminers.net/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Information Shaping for Enhanced Goal Recognition of Partially-Informed Agents

2020 ◽  
Vol 34 (06) ◽  
pp. 9908-9915
Author(s):  
Sarah Keren ◽  
Haifeng Xu ◽  
Kofi Kwapong ◽  
David Parkes ◽  
Barbara Grosz
Keyword(s):  
Incomplete Information ◽  
Recognition Task ◽  
Search Space ◽  
Perfect Information ◽  
Suggested Method ◽  
Information Revelation ◽  
Goal Recognition ◽  
Effectiveness And Efficiency

We extend goal recognition design to account for partially informed agents. In particular, we consider a two-agent setting in which one agent, the actor, seeks to achieve a goal but has only incomplete information about the environment. The second agent, the recognizer, has perfect information and aims to recognize the actor's goal from its behavior as quickly as possible. As a one-time offline intervention and with the objective of facilitating the recognition task, the recognizer can selectively reveal information to the actor. The problem of selecting which information to reveal, which we call information shaping, is challenging not only because the space of information shaping options may be large, but also because more information revelation need not make it easier to recognize an agent's goal. We formally define this problem, and suggest a pruning approach for efficiently searching the search space. We demonstrate the effectiveness and efficiency of the suggested method on standard benchmarks.

scholarly journals Generalization and search in risky environments

2017 ◽  
Author(s):  
Eric Schulz ◽  
Charley M. Wu ◽  
Quentin J. M. Huys ◽  
Andreas Krause ◽  
Maarten Speekenbrink
Keyword(s):  
Cross Validation ◽  
Learning Algorithm ◽  
Search Space ◽  
Risk And Uncertainty ◽  
Exploration And Exploitation ◽  
Function Learning ◽  
Spatially Correlated ◽  
Sampling Behavior ◽  
The Face ◽  
Optimization Routine

AbstractHow do people pursue rewards in risky environments, where some outcomes should be avoided at all costs? We investigate how participant search for spatially correlated rewards in scenarios where one must avoid sampling rewards below a given threshold. This requires not only the balancing of exploration and exploitation, but also reasoning about how to avoid potentially risky areas of the search space. Within risky versions of the spatially correlated multi-armed bandit task, we show that participants’ behavior is aligned well with a Gaussian process function learning algorithm, which chooses points based on a safe optimization routine. Moreover, using leave-one-block-out cross-validation, we find that participants adapt their sampling behavior to the riskiness of the task, although the underlying function learning mechanism remains relatively unchanged. These results show that participants can adapt their search behavior to the adversity of the environment and enrich our understanding of adaptive behavior in the face of risk and uncertainty.

MINING WEB USAGE GRAPHS USING EXAMPLE SEARCH SPACE

2002 ◽  
Vol 02 (02) ◽  
pp. 209-220 ◽  
Author(s):  
V. UMA MAHESWARI ◽  
A. SIROMONEY ◽  
K. M. MEHATA
Keyword(s):  
Web Mining ◽  
Learning Algorithm ◽  
Search Space ◽  
Web Data ◽  
Web Usage ◽  
Navigation Patterns ◽  
Example Space

Web mining refers to the process of discovering potentially useful and previously unknown information or knowledge from web data. A graph-based framework is used for classifying Web users based on their navigation patterns. GOLEM is a learning algorithm that uses the example space to restrict the solution search space. In this paper, this algorithm is modified for the graph-based framework. GOLEM is appropriate in this application where the solution search space is very large. An experimental illustration is presented.

scholarly journals Bias Free Multiobjective Active Learning for Materials Design and Discovery

2020 ◽  
Author(s):  
Kevin Maik Jablonka ◽  
Giriprasad Melpatti Jothiappan ◽  
Shefang Wang ◽  
Berend Smit ◽  
Brian Yoo
Keyword(s):  
Active Learning ◽  
De Novo ◽  
Learning Algorithm ◽  
Random Search ◽  
Search Space ◽  
Machine Learning Techniques ◽  
Pareto Optimal ◽  
Design Rules ◽  
Learning Techniques ◽  
Screening Approaches

<div>The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material, and the design rules change to finding the set of Pareto optimal materials. </div><div>In this work, we introduce an active learning algorithm that directly uses the Pareto dominance relation to compute the set of Pareto optimal materials with desirable accuracy. <br></div><div>We apply our algorithm to de novo polymer design with a prohibitively large search space.</div><div>Using molecular simulations, we compute key descriptors for dispersant applications and reduce the number of materials that need to be evaluated to reconstruct the Pareto front with a desired confidence by over 98% compared to random search.</div><div>This work showcases how simulation and machine learning techniques can be coupled to discover materials within a design space that would be intractable using conventional screening approaches.</div>

scholarly journals Bias Free Multiobjective Active Learning for Materials Design and Discovery

2020 ◽  
Author(s):  
Kevin Maik Jablonka ◽  
Giriprasad Melpatti Jothiappan ◽  
Shefang Wang ◽  
Berend Smit ◽  
Brian Yoo
Keyword(s):  
Active Learning ◽  
De Novo ◽  
Learning Algorithm ◽  
Random Search ◽  
Search Space ◽  
Machine Learning Techniques ◽  
Pareto Optimal ◽  
Design Rules ◽  
Learning Techniques ◽  
Screening Approaches

<div>The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material, and the design rules change to finding the set of Pareto optimal materials. </div><div>In this work, we introduce an active learning algorithm that directly uses the Pareto dominance relation to compute the set of Pareto optimal materials with desirable accuracy. <br></div><div>We apply our algorithm to de novo polymer design with a prohibitively large search space.</div><div>Using molecular simulations, we compute key descriptors for dispersant applications and reduce the number of materials that need to be evaluated to reconstruct the Pareto front with a desired confidence by over 98% compared to random search.</div><div>This work showcases how simulation and machine learning techniques can be coupled to discover materials within a design space that would be intractable using conventional screening approaches.</div>

scholarly journals A Novel Adaptive LMS Algorithm with Genetic Search Capabilities for System Identification of Adaptive FIR and IIR Filters

Information ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 176 ◽  
Author(s):  
Amjad J. Humaidi ◽  
Ibraheem Kasim Ibraheem ◽  
Ahmed R. Ajel
Keyword(s):  
System Identification ◽  
Input Signal ◽  
Digital Filters ◽  
Learning Algorithm ◽  
Search Space ◽  
Lms Algorithm ◽  
Adaptation Algorithm ◽  
Genetic Search ◽  
Mutation Operators ◽  
Input Signals

In this paper we introduce a novel adaptation algorithm for adaptive filtering of FIR and IIR digital filters within the context of system identification. The standard LMS algorithm is hybridized with GA (Genetic Algorithm) to obtain a new integrated learning algorithm, namely, LMS-GA. The main aim of the proposed learning tool is to evade local minima, a common problem in standard LMS algorithm and its variants and approaching the global minimum by calculating the optimum parameters of the weights vector when just estimated data are accessible. In the proposed LMS-GA technique, first, it works as the standard LMS algorithm and calculates the optimum filter coefficients that minimize the mean square error, once the standard LMS algorithm gets stuck in local minimum, the LMS-GA switches to GA to update the filter coefficients and explore new region in the search space by applying the cross-over and mutation operators. The proposed LMS-GA is tested under different conditions of the input signal like input signals with colored characteristics, i.e., correlated input signals and investigated on FIR adaptive filter using the power spectral density of the input signal and the Fourier-transform of the input’s correlation matrix. Demonstrations via simulations on system identification of IIR and FIR adaptive digital filters revealed the effectiveness of the proposed LMS-GA under input signals with different characteristics.

Harnessing machine learning to boost heuristic strategies for phylogenetic-tree search

2020 ◽  
Author(s):  
Dana Azouri ◽  
Shiran Abadi ◽  
Yishay Mansour ◽  
Itay Mayrose ◽  
Tal Pupko
Keyword(s):  
Machine Learning ◽  
Phylogenetic Tree ◽  
Learning Algorithm ◽  
Search Space ◽  
Large Set ◽  
Tree Search ◽  
Learning Approaches ◽  
Tree Reconstruction ◽  
Heuristic Strategies ◽  
Tree Inference

Abstract Inferring a phylogenetic tree, which describes the evolutionary relationships among a set of organisms, genes, or genomes, is a fundamental step in numerous evolutionary studies. With the aim of making tree inference feasible for problems involving more than a handful of sequences, current algorithms for phylogenetic tree reconstruction utilize various heuristic approaches. Such approaches rely on performing costly likelihood optimizations, and thus evaluate only a subset of all potential trees. Consequently, all existing methods suffer from the known tradeoff between accuracy and running time. Here, we train a machine-learning algorithm over an extensive cohort of empirical data to predict the neighboring trees that increase the likelihood, without actually computing their likelihood. This provides means to safely discard a large set of the search space, thus avoiding numerous expensive likelihood computations. Our analyses suggest that machine-learning approaches can make heuristic tree searches substantially faster without losing accuracy and thus could be incorporated for narrowing down the examined neighboring trees of each intermediate tree in any tree search methodology.

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