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255 results for “Problem solving”
Individual Differences in Fluid Reasoning and RAPM-like Problem Solving
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A framework for step-wise explaining how to solve constraint satisfaction problems
<p>We explore the problem of step-wise explaining how to solve constraint satisfaction problems, with a use case on logic grid puzzles. More specifically, we study the problem of explaining the inference steps that one can take during propagation, in a way that is easy to interpret for a person. Thereby, we aim to give the constraint solver explainable agency, which can help in building trust in the solver by being able to understand and even learn from the explanations. The main challenge is that of finding a sequence of simple explanations, where each explanation should aim to be as cognitively easy as possible for a human to verify and understand. This contrasts with the arbitrary combination of facts and constraints that the solver may use when propagating. We propose the use of a cost function to quantify how simple an individual explanation of an inference step is, and identify the explanation-production problem of finding the best sequence of explanations of a CSP. Our approach is agnostic of the underlying constraint propagation mechanisms, and can provide explanations even for inference steps resulting from combinations of constraints. In case multiple constraints are involved, we also develop a mechanism that allows to break the most difficult steps up and thus gives the user the ability to zoom in on specific parts of the explanation. Our proposed algorithm iteratively constructs the explanation sequence by using an optimistic estimate of the cost function to guide the search for the best explanation at each step. Our experiments on logic grid puzzles show the feasibility of the approach in terms of the quality of the individual explanations and the resulting explanation sequences obtained.</p>
Supplemental material to "Solving Quantified Modal Logic Problems by Translation to Classical Logics"
<p>These files are associated with the manuscript entitled<br>"Solving Quantified Modal Logic Problems by Translation to Classical Logics"<br>by Alexander Steen, Geoff Sutcliffe, Christoph Benzmüller.</p> <p>Contact: Alexander Steen <alexander.steen@uni-greifswald.de></p> <p>Contents<br>-----------</p> <p> - QMLTP-monomodal-NX0.tar.gz<br> This archive contains the TPTP NX0 representations of the 580 mono-modal<br> problems translated from the QMLTP library [1,2].<br> <br> - QMLTP-monomodal-TF0-embedded-rigid-local.tar.gz<br> This archive contains the embedded TF0 files created <br> from the monomodal NX0 files using the Logic Embedding Tool [3].<br> <br> - QMLTP-monomodal-TH0-embedded-rigid-local.tar.gz<br> This archive contains the embedded TH0 files created <br> from the monomodal NX0 files using the Logic Embedding Tool [3,4].<br> <br> - QMLTP-multimodal-NX0-and-embedded.tar.gz<br> This archive contains the TPTP NX0 representations of the 20 multi-modal<br> problems translated from the QMLTP library [1,2]. Additionally, it<br> contains the 20 embedded TF0 and the 20 embedded THF files created <br> from the NX0 files using the Logic Embedding Tool [3,4].<br> <br> - QMLTP-primary-evaluation-results-QMLTP.zip<br> This archive contains the primary evaluation data creating from<br> running E 3.0.03, Leo-III 1.7.8, Nitpick 2016, Vampire 4.8, <br> MleanCoP 1.3, nanoCoP-M 2.0 on the problem files.<br> All reasoning systems except Nitpick were run on the StarExec Miami cluster with a 60s<br> wall clock and 480 CPU time limit. The StarExec Miami computers have an<br> octa-core Intel Xeon E5-2667 3.20 GHz CPU, 128 GiB memory, and run the<br> CentOS Linux release 7.4.1708 operating system. Nitpick was run on a server<br> with a 60s wall clock time limit. The server has an octa-core Intel Xeon E5-<br> 2609 2.50 GHz CPU, 64 GiB memory, and the CentOS Linux release 7.9.2009<br> operating system.<br> <br> - README<br> This file.<br> <br> <br> <br>[1] T. Raths and J. Otten. The QMLTP Problem Library for First-Order Modal Logics.<br> In B. Gramlich, D. Miller, and U. Sattler, editors, Proceedings of the 6th International Joint Conference on Automated Reasoning,<br> number 7364 in Lecture Notes in Artificial Intelligence, pages 454–461. Springer, 2012.<br>[2] http://www.iltp.de/qmltp/<br>[3] A. Steen. An extensible logic embedding tool for lightweight non-classical reasoning (short paper).<br> In B. Konev, C. Schon, and A. Steen, editors, Proceedings of the 8th Workshop on Practical Aspects of Automated<br> Reasoning, number 3201 in CEUR Workshop Proceedings, 2022.<br>[4] https://github.com/leoprover/logic-embedding</p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 7. Execution plot, for instance Eil51 (left figure) and KroB100 (right figure)
<p>The evolution of the best solution found by the proposed algorithm is plotted in Figure 7 during a typical execution when solving instance Eil51 and KroB100. In this figure, the horizontal and vertical axes show the number of iterations and gained values of the proposed algorithm respectively. Besides, there is a fast convergence toward the BKS at the beginning of the execution while in the rest of the search the evolution of the BKS is not that fast. </p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 5. The process of MICALK for solving the TSP
<p>Moreover, in order to prevent the ICA from getting trapped in stagnation, we used a local searching algorithm when the algorithm attained a better solution compared to previous iterations. In fact, the probability of finding better solutions near a good solution is relatively high. There exist many algorithms for the local search and they have of course their pros and cons. Since LinKernighan algorithm is simple and it is one of the most successful methods for generating optimal or near optimal solutions for the TSP, we have used it in this study. The main steps of MICALK are summarized in the pseudo-code given in Figure 5. </p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 6. Some best routes found by the proposed algorithm
<p>Figure 6 shows some of the best solutions searched by the proposed method. In this figure, the horizontal axis represents the x-axis with increasing positive values to the right and the vertical axis represents the y-axis with increasing positive values upward. </p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 3. Flowchart of the ICA
<p>At last, the most powerful empire will take the possession of other empires and will win the competition. In other words, imperialistic competition hopefully converges to a state in which there exists only one empire and its colonies are in the same position and have the same cost as the imperialist. Figure 3 shows the flowchart of the basic ICA.</p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 1. The Initial Empires
<p>The ICA is a novel global search strategy which uses imperialism and imperialistic competition process as a source of inspiration. This algorithm is based on the fact that in a real world, countries try to extend their power over other countries in order to use their resources and bolster their own government. The first step in ICA is to generate an initial population like other evolutionary algorithms. The population set includes a number of feasible solutions called a ‘country’, which corresponds to the term ‘chromosome’ in the GA method. These countries are of two types: colonies and imperialists that altogether form some empires. As it is shown in Figure 1 (Atashpaz Gargari & Lucas, 2007), bigger and stronger empires have more colonies than smaller and weaker ones.</p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 2. Eliminate the weakest colony of the weakest empire
<p>After initial empires are formed, their colonies start moving toward their relevant imperialist country. This movement is a simple model of assimilation policy which was pursued by some of the imperialist states. If one of the colonies possesses more power than its relevant imperialist after this movement, they will exchange their positions. To begin the competition between empires, the total objective function of each empire should be calculated. It depends on the objective function of both an imperialist and its colonies. Imperialistic competition among these empires forms the basis of the proposed evolutionary algorithm. During this competition, weak empires collapse and powerful ones take the possession of their colonies - Figure 2 (Atashpaz Gargari & Lucas, 2007). The empire, which has lost all its colonies, will collapse. </p>
Remarkable Problem-Solving Ability of Unicellular Amoeboid Organism and its Mechanism
<p>Supplementary Materials of "Remarkable Problem-Solving Ability of Unicellular Amoeboid Organism and its Mechanism."</p>
Xcompact3D: An open-source framework for solving turbulence problems on a Cartesian mesh
<p>Contains figures used in + the data with scripts to generate figures for the manuscript (in preparation)</p> <p>Xcompact3D: An open-source framework for solving turbulence problems on a Cartesian mesh</p>
Pilot Study of Peer-Supported Online Problem-Solving Program
ClinicalTrials.gov study NCT03555435. IPD Sharing: YES. Countries: 1. Publications: 1.
Enhanced Problem-Solving Training
ClinicalTrials.gov study NCT03759223. IPD Sharing: YES. Countries: 1. Publications: 2.
Data from: Thinking outside the box: problem-solving in free-living lizards
<p>Despite evidence that organisms are more likely to exhibit their full range of cognitive abilities under conditions found in nature, studies evaluating cognition under such conditions remain rare, particularly in vertebrate species. Here we conducted an experiment to evaluate problem solving and motor-self regulation in free-living arboreal lizards, Anolis sagrei, under natural conditions. We presented lizards with a novel detour problem which challenged individuals to circumvent a transparent barrier in order to obtain a food reward. Individuals varied in their ability to solve the detour problem. Furthermore, those that solved the problem were able to improve their performance across trials by modifying the natural response of attempting to strike the reward trough the transparent barrier, providing evidence of motor self-regulation. Solving the problem required individuals to modify their typical foraging behavior, as approaching the prey in a single burst of movement that culminated with an attack was an unsuccessful strategy. Contrary to expectations our findings provide evidence of motor self-regulation in a visually-oriented, sit-and-wait predator under natural conditions, suggesting motor-self-regulation is not limited by foraging strategy. Our results also underscore the need to evaluate the cognitive abilities of free-living organisms in the wild, particularly for taxa that perform poorly under laboratory conditions.</p>
Learning Active Constraints to Efficiently Solve Bilevel Problems
<p>Datafiles for the test cases in the article <a href="https://arxiv.org/abs/2010.06344">"Learning Active Constraints to Efficiently Solve Bilevel Problems"</a>. The corresponding code is available on <a href="https://github.com/eleaprat/Bilevel---Active-Constraints">GitHub</a>.</p>
Data from: Females can solve the problem of low signal reliability by assessing multiple male traits
Male signals that provide information to females about mating benefits are often of low reliability. It is thus not clear why females often express strong signal preferences. We tested the hypothesis that females can distinguish between males with preferred signals that provide lower and higher quality direct benefits. In the field cricket, Gryllus lineaticeps, females usually prefer higher male chirp rates, but chirp rate is positively correlated with the fecundity benefits females will receive from males only for males that have experienced low quality diets. We paired females with muted males that were maintained on low or high nutrition diets, during the interactions we broadcast a replacement high chirp rate, and we observed whether females mated with the assigned male. Females were more likely to mate when paired with low nutrition males. These results suggest that females have evolved assessment mechanisms that allow them distinguish between males with preferred signals that provide high quality benefits (low nutrition males with high chirp rates) and males with preferred signals that provide low quality benefits (high nutrition males with high chirp rates).
Problem-solving skills are predicted by innovations in the wild and brain size in Passerines
<p>Code and dataset for the article "<strong>Problem-solving skills are predicted by innovations in the wild and brain size in Passerines</strong>"</p>
Problem solving booklet
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Data for: Serotonin transporter (SERT) polymorphisms, personality and problem-solving in urban great tits
<p class="CxSpFirst"><span><span><span><span><span><span><span><span><span><span><span>Understanding underlying genetic variation can elucidate how diversity in behavioral phenotypes evolves and is maintained. Genes in the serotonergic signaling pathway, including the serotonin transporter gene (<i>SERT)</i>, are candidates for affecting animal personality, cognition and fitness. In a model species, the great tit (<i>Parus major</i>), we reevaluated previous findings suggesting relationships between <i>SERT</i> polymorphisms, neophobia, exploratory behavior and fitness parameters, and performed a first test of the relationship between single nucleotide polymorphisms (SNPs) in SERT and problem-solving in birds. We found some evidence for associations between <i>SERT </i>SNPs and neophobia, exploratory behavior and laying date. Furthermore, several SNPs were associated with behavioral patterns and success rates during obstacle removal problem-solving tests performed at nest boxes. In females, minor allele homozygotes (AA) for nonsynonymous SNP226 in exon 1 made fewer incorrect attempts and were more likely to problem-solve. In both sexes, there was some evidence that minor allele homozygotes (CC) for SNP84 in exon 9 were more likely to problem-solve. Only one SNP-behavior relationship was statistically significant after correcting for multiple comparisons, but several were associated with substantial effect sizes. Our study provides a foundation for future research on the genetic basis of behavioral and cognitive variation in wild animal populations. </span></span></span></span></span></span></span></span></span></span></span></p>
Image Data Part 1 of AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem
<p>Image Data Part 1 of AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem</p> <p>Paper: https://ieeexplore.ieee.org/document/9497733/</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.