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2020 – today
- 2024
[j38]George Paterakis
, Stefanos Fafalios, Paulos Charonyktakis, Vassilis Christophides, Ioannis Tsamardinos:
Do We Really Need Imputation in AutoML Predictive Modeling? ACM Trans. Knowl. Discov. Data 18(6): 147:1-147:64 (2024)- [j37]Konstantina Biza, Ioannis Tsamardinos, Sofia Triantafillou:
Out-of-Sample Tuning for Causal Discovery. IEEE Trans. Neural Networks Learn. Syst. 35(4): 4963-4973 (2024) - [c55]Nikolaos Myrtakis, Ioannis Tsamardinos, Vassilis Christophides:
AutoML for Explainable Anomaly Detection (XAD). Tannen's Festschrift 2024: 8:1-8:23 - [i16]Konstantina Biza, Antonios Ntroumpogiannis, Sofia Triantafillou, Ioannis Tsamardinos:
Towards Automated Causal Discovery: a case study on 5G telecommunication data. CoRR abs/2402.14481 (2024) - [i15]Konstantinos Paraschakis, Andrea Castellani, Giorgos Borboudakis, Ioannis Tsamardinos:
Confidence Interval Estimation of Predictive Performance in the Context of AutoML. CoRR abs/2406.08099 (2024) - 2023
- [j36]Kleanthi Lakiotaki, Zacharias Papadovasilakis, Vincenzo Lagani, Stefanos Fafalios, Paulos Charonyktakis, Michail Tsagris, Ioannis Tsamardinos:
Automated machine learning for genome wide association studies. Bioinform. 39(9) (2023) - [j35]Ioulia Karagiannaki, Krystallia Gourlia, Vincenzo Lagani, Yannis Pantazis, Ioannis Tsamardinos:
Learning biologically-interpretable latent representations for gene expression data. Mach. Learn. 112(11): 4257-4287 (2023) - [j34]Antonios Ntroumpogiannis, Michail Giannoulis, Nikolaos Myrtakis, Vassilis Christophides, Eric Simon, Ioannis Tsamardinos:
A meta-level analysis of online anomaly detectors. VLDB J. 32(4): 845-886 (2023) - [i14]Giorgos Borboudakis, Paulos Charonyktakis, Konstantinos Paraschakis, Ioannis Tsamardinos:
A Meta-Level Learning Algorithm for Sequential Hyper-Parameter Space Reduction in AutoML. CoRR abs/2312.06305 (2023) - 2022
- [j33]Vasilios Plakandaras, Periklis Gogas, Theophilos Papadimitriou, Ioannis Tsamardinos:
Credit Card Fraud Detection with Automated Machine Learning Systems. Appl. Artif. Intell. 36(1) (2022) - [j32]Ioannis Tsamardinos:
Don't lose samples to estimation. Patterns 3(12): 100612 (2022) - [j31]Michail Tsagris, Zacharias Papadovasilakis, Kleanthi Lakiotaki, Ioannis Tsamardinos:
The $\gamma$γ-OMP Algorithm for Feature Selection With Application to Gene Expression Data. IEEE ACM Trans. Comput. Biol. Bioinform. 19(2): 1214-1224 (2022) - [i13]Antonios Ntroumpogiannis, Michail Giannoulis, Nikolaos Myrtakis, Vassilis Christophides, Eric Simon, Ioannis Tsamardinos:
A Meta-level Analysis of Online Anomaly Detectors. CoRR abs/2209.05899 (2022) - 2021
- [j30]Giorgos Borboudakis, Ioannis Tsamardinos:
Extending greedy feature selection algorithms to multiple solutions. Data Min. Knowl. Discov. 35(4): 1393-1434 (2021) - [c54]Maria Papadogiorgaki, Maria Venianaki, Paulos Charonyktakis, Marios Antonakakis, Ioannis Tsamardinos, Michalis E. Zervakis, Vangelis Sakkalis:
Heart Rate Classification Using ECG Signal Processing and Machine Learning Methods. BIBE 2021: 1-6 - [c53]Nikolaos Myrtakis, Ioannis Tsamardinos, Vassilis Christophides:
PROTEUS: Predictive Explanation of Anomalies. ICDE 2021: 1967-1972 - [i12]Nikolaos Myrtakis, Ioannis Tsamardinos, Vassilis Christophides:
On Predictive Explanation of Data Anomalies. CoRR abs/2110.09467 (2021) - 2020
- [c52]Yannis Pantazis, Christos Tselas, Kleanthi Lakiotaki, Vincenzo Lagani, Ioannis Tsamardinos:
Latent Feature Representations for Human Gene Expression Data Improve Phenotypic Predictions. BIBM 2020: 2505-2512 - [c51]Ioulia Karagiannaki, Yannis Pantazis, Ekaterini Chatzaki, Ioannis Tsamardinos:
Pathway Activity Score Learning for Dimensionality Reduction of Gene Expression Data. DS 2020: 246-261 - [c50]Iordanis Xanthopoulos, Ioannis Tsamardinos, Vassilis Christophides, Eric Simon, Alejandro Salinger:
Putting the Human Back in the AutoML Loop. EDBT/ICDT Workshops 2020 - [c49]Konstantina Biza, Ioannis Tsamardinos, Sofia Triantafillou:
Tuning Causal Discovery Algorithms. PGM 2020: 17-28 - [i11]Michail Tsagris, Zacharias Papadovasilakis, Kleanthi Lakiotaki, Ioannis Tsamardinos:
A generalised OMP algorithm for feature selection with application to gene expression data. CoRR abs/2004.00281 (2020) - [i10]Anastasios Tsourtis, Yannis Pantazis, Ioannis Tsamardinos:
Inference of Stochastic Dynamical Systems from Cross-Sectional Population Data. CoRR abs/2012.05055 (2020)
2010 – 2019
- 2019
- [j29]Yannis Pantazis, Ioannis Tsamardinos:
A unified approach for sparse dynamical system inference from temporal measurements. Bioinform. 35(18): 3387-3396 (2019) - [j28]Giorgos Borboudakis, Ioannis Tsamardinos:
Forward-Backward Selection with Early Dropping. J. Mach. Learn. Res. 20: 8:1-8:39 (2019) - [j27]Ioannis Tsamardinos, Giorgos Borboudakis, Pavlos Katsogridakis, Polyvios Pratikakis, Vassilis Christophides:
A greedy feature selection algorithm for Big Data of high dimensionality. Mach. Learn. 108(2): 149-202 (2019) - 2018
- [j26]Kleanthi Lakiotaki, Nikolaos Vorniotakis, Michail Tsagris, Georgios Georgakopoulos, Ioannis Tsamardinos:
BioDataome: a collection of uniformly preprocessed and automatically annotated datasets for data-driven biology. Database J. Biol. Databases Curation 2018: bay011 (2018) - [j25]Michail Tsagris, Vincenzo Lagani, Ioannis Tsamardinos:
Feature selection for high-dimensional temporal data. BMC Bioinform. 19(1): 17:1-17:14 (2018) - [j24]Konstantinos Tsirlis, Vincenzo Lagani, Sofia Triantafillou, Ioannis Tsamardinos:
On scoring Maximal Ancestral Graphs with the Max-Min Hill Climbing algorithm. Int. J. Approx. Reason. 102: 74-85 (2018) - [j23]Michail Tsagris, Giorgos Borboudakis, Vincenzo Lagani, Ioannis Tsamardinos:
Constraint-based causal discovery with mixed data. Int. J. Data Sci. Anal. 6(1): 19-30 (2018) - [j22]Michail Tsagris, Giorgos Borboudakis, Vincenzo Lagani, Ioannis Tsamardinos:
Correction to: Constraint-based causal discovery with mixed data. Int. J. Data Sci. Anal. 6(1): 31 (2018) - [j21]Ioannis Tsamardinos, Elissavet Greasidou, Giorgos Borboudakis:
Bootstrapping the out-of-sample predictions for efficient and accurate cross-validation. Mach. Learn. 107(12): 1895-1922 (2018) - [c48]Marios Adamou, Grigoris Antoniou, Elissavet Greasidou, Vincenzo Lagani, Paulos Charonyktakis, Ioannis Tsamardinos:
Mining Free-Text Medical Notes for Suicide Risk Assessment. SETN 2018: 47:1-47:8 - [i9]Michail Tsagris, Ioannis Tsamardinos:
Feature selection with the R package MXM. F1000Research 7: 1505 (2018) - 2017
- [j20]Giorgos Papoutsoglou, Giorgos Athineou, Vincenzo Lagani, Iordanis Xanthopoulos, Angelika Schmidt, Szabolcs Elias, Jesper Tegnér, Ioannis Tsamardinos:
SCENERY: a web application for (causal) network reconstruction from cytometry data. Nucleic Acids Res. 45(Webserver-Issue): W270-W275 (2017) - [i8]Giorgos Borboudakis, Ioannis Tsamardinos:
Forward-Backward Selection with Early Dropping. CoRR abs/1705.10770 (2017) - [i7]Ioannis Tsamardinos, Giorgos Borboudakis, Pavlos Katsogridakis, Polyvios Pratikakis, Vassilis Christophides:
Massively-Parallel Feature Selection for Big Data. CoRR abs/1708.07178 (2017) - [i6]Ioannis Tsamardinos, Elissavet Greasidou, Michalis Tsagris, Giorgos Borboudakis:
Bootstrapping the Out-of-sample Predictions for Efficient and Accurate Cross-Validation. CoRR abs/1708.07180 (2017) - 2016
- [j19]Vincenzo Lagani, Argyro D. Karozou, David Gomez-Cabrero, Gilad Silberberg, Ioannis Tsamardinos:
Erratum to: A comparative evaluation of data-merging and meta-analysis methods for reconstructing gene-gene interactions. BMC Bioinform. 17: 290 (2016) - [j18]Vincenzo Lagani, Argyro D. Karozou, David Gomez-Cabrero, Gilad Silberberg, Ioannis Tsamardinos:
A comparative evaluation of data-merging and meta-analysis methods for reconstructing gene-gene interactions. BMC Bioinform. 17(S-5): S194 (2016) - [j17]Paulos Charonyktakis, Maria Plakia, Ioannis Tsamardinos, Maria Papadopouli:
On User-Centric Modular QoE Prediction for VoIP Based on Machine-Learning Algorithms. IEEE Trans. Mob. Comput. 15(6): 1443-1456 (2016) - [c47]Giorgos Borboudakis, Ioannis Tsamardinos:
Towards Robust and Versatile Causal Discovery for Business Applications. KDD 2016: 1435-1444 - [c46]Giorgos Athineou, Giorgos Papoutsoglou, Sofia Triantafillou, Ioannis Basdekis, Vincenzo Lagani, Ioannis Tsamardinos:
SCENERY: A Web-Based Application for Network Reconstruction and Visualization of Cytometry Data. PACBB 2016: 203-211 - [c45]Anna Roumpelaki, Giorgos Borboudakis, Sofia Triantafillou, Ioannis Tsamardinos:
Marginal Causal Consistency in Constraint-based Causal Learning. CFA@UAI 2016: 39-47 - [c44]Sofia Triantafillou, Ioannis Tsamardinos:
Score-based vs Constraint-based Causal Learning in the Presence of Confounders. CFA@UAI 2016: 59-67 - 2015
- [j16]Ioannis Tsamardinos, Amin Rakhshani, Vincenzo Lagani:
Performance-Estimation Properties of Cross-Validation-Based Protocols with Simultaneous Hyper-Parameter Optimization. Int. J. Artif. Intell. Tools 24(5): 1540023:1-1540023:29 (2015) - [j15]Sofia Triantafillou, Ioannis Tsamardinos:
Constraint-based causal discovery from multiple interventions over overlapping variable sets. J. Mach. Learn. Res. 16: 2147-2205 (2015) - [c43]Christina Papagiannopoulou, Grigorios Tsoumakas, Ioannis Tsamardinos:
Discovering and Exploiting Deterministic Label Relationships in Multi-Label Learning. KDD 2015: 915-924 - [c42]Grace T. Huang, Ioannis Tsamardinos, Vineet K. Raghu, Naftali Kaminski, Panayiotis V. Benos:
T-ReCS: Stable Selection of Dynamically Formed Groups of Features with Application to Prediction of Clinical Outcomes. Pacific Symposium on Biocomputing 2015: 431-442 - [c41]Giorgos Borboudakis, Ioannis Tsamardinos:
Bayesian Network Learning with Discrete Case-Control Data. UAI 2015: 151-160 - 2014
- [j14]Nestoras Karathanasis, Ioannis Tsamardinos, Panayiota Poirazi:
Don't use a cannon to kill the ... miRNA mosquito. Bioinform. 30(7): 1047-1048 (2014) - [c40]Sofia Triantafilou, Ioannis Tsamardinos, Anna Roumpelaki:
Learning Neighborhoods of High Confidence in Constraint-Based Causal Discovery. Probabilistic Graphical Models 2014: 487-502 - [c39]Ioannis Tsamardinos, Amin Rakhshani, Vincenzo Lagani:
Performance-Estimation Properties of Cross-Validation-Based Protocols with Simultaneous Hyper-Parameter Optimization. SETN 2014: 1-14 - [i5]Sofia Triantafilou, Ioannis Tsamardinos:
Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Sets. CoRR abs/1403.2150 (2014) - [i4]Christina Papagiannopoulou, Grigorios Tsoumakas, Ioannis Tsamardinos:
Discovering and Exploiting Entailment Relationships in Multi-Label Learning. CoRR abs/1404.4038 (2014) - [i3]Giorgos Borboudakis, Ioannis Tsamardinos:
Scoring and Searching over Bayesian Networks with Causal and Associative Priors. CoRR abs/1408.2057 (2014) - 2013
- [c38]Nestoras Karathanasis, Ioannis Tsamardinos, Panayiota Poirazi:
A bioinformatics approach for investigating the determinants of Drosha processing. BIBE 2013: 1-4 - [c37]Giorgos Borboudakis, Ioannis Tsamardinos:
Scoring and Searching over Bayesian Networks with Causal and Associative Priors. UAI 2013 - 2012
- [j13]Laura E. Brown, Ioannis Tsamardinos, Douglas P. Hardin:
To feature space and back: Identifying top-weighted features in polynomial Support Vector Machine models. Intell. Data Anal. 16(4): 551-579 (2012) - [j12]Ioannis Tsamardinos, Sofia Triantafilou, Vincenzo Lagani:
Towards Integrative Causal Analysis of Heterogeneous Data Sets and Studies. J. Mach. Learn. Res. 13: 1097-1157 (2012) - [c36]Nestoras Karathanasis, Ioannis Tsamardinos, Angelos P. Armen, Panayiota Poirazi:
SVM-based miRNA: MiRNA∗ duplex prediction. BIBE 2012: 181-186 - [c35]Giorgos Borboudakis, Ioannis Tsamardinos:
Incorporating Causal Prior Knowledge as Path-Constraints in Bayesian Networks and Maximal Ancestral Graphs. ICML 2012 - [c34]Sophia Kleisarchaki, Dimitris Kotzinos, Ioannis Tsamardinos, Vassilis Christophides:
A Methodological Framework for Statistical Analysis of Social Text Streams. ISIP 2012: 101-110 - [c33]Vincenzo Lagani, Ioannis Tsamardinos, Sofia Triantafilou:
Learning from Mixture of Experimental Data: A Constraint-Based Approach. SETN 2012: 124-131 - [i2]Giorgos Borboudakis, Ioannis Tsamardinos:
Scoring Bayesian Networks with Informative, Causal and Associative Priors. CoRR abs/1209.6561 (2012) - 2011
- [c32]Chrysi Filippaki, Grigoris Antoniou, Ioannis Tsamardinos:
Using Constraint Optimization for Conflict Resolution and Detail Control in Activity Recognition. AmI 2011: 51-60 - [c31]Angelos P. Armen, Ioannis Tsamardinos:
A unified approach to estimation and control of the False Discovery Rate in Bayesian network skeleton identification. ESANN 2011 - [c30]Giorgos Borboudakis, Sofia Triantafilou, Vincenzo Lagani, Ioannis Tsamardinos:
A constraint-based approach to incorporate prior knowledge in causal models. ESANN 2011 - [c29]Eleni G. Christodoulou, O. D. Røe, Amos Folarin, Ioannis Tsamardinos:
Information-Preserving Techniques Improve Chemosensitivity Prediction of Tumours Based on Expression Profiles. EANN/AIAI (1) 2011: 453-462 - [c28]Lefteris Koumakis, Franco Chiarugi, Vincenzo Lagani, Angelina Kouroubali, Ioannis Tsamardinos:
Risk Assessment Models for Diabetes Complications: A Survey of Available Online Tools. MobiHealth 2011: 46-53 - 2010
- [j11]Vincenzo Lagani, Ioannis Tsamardinos:
Structure-based variable selection for survival data. Bioinform. 26(15): 1887-1894 (2010) - [j10]Constantin F. Aliferis, Alexander R. Statnikov, Ioannis Tsamardinos, Subramani Mani, Xenofon D. Koutsoukos:
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation. J. Mach. Learn. Res. 11: 171-234 (2010) - [j9]Constantin F. Aliferis, Alexander R. Statnikov, Ioannis Tsamardinos, Subramani Mani, Xenofon D. Koutsoukos:
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and Extensions. J. Mach. Learn. Res. 11: 235-284 (2010) - [c27]Ioannis Tsamardinos, Giorgos Borboudakis:
Permutation Testing Improves Bayesian Network Learning. ECML/PKDD (3) 2010: 322-337 - [c26]Sofia Triantafilou, Ioannis Tsamardinos, Ioannis G. Tollis:
Learning Causal Structure from Overlapping Variable Sets. AISTATS 2010: 860-867
2000 – 2009
- 2009
- [c25]Ioannis Tsamardinos, Asimakis P. Mariglis:
Multi-Source Causal Analysis: Learning Bayesian Networks from Multiple Datasets. AIAI 2009: 479-490 - 2008
- [c24]Ioannis Tsamardinos, Laura E. Brown:
Bounding the False Discovery Rate in Local Bayesian Network Learning. AAAI 2008: 1100-1105 - [c23]Laura E. Brown, Ioannis Tsamardinos:
A Strategy for Making Predictions Under Manipulation. WCCI Causation and Prediction Challenge 2008: 35-52 - 2007
- [i1]Ioannis Tsamardinos:
Causal Data Mining in Bioinformatics. ERCIM News 2007(69) (2007) - 2006
- [j8]Ioannis Tsamardinos, Laura E. Brown, Constantin F. Aliferis:
The max-min hill-climbing Bayesian network structure learning algorithm. Mach. Learn. 65(1): 31-78 (2006) - [c22]Ioannis Tsamardinos, Alexander R. Statnikov, Laura E. Brown, Constantin F. Aliferis:
Generating Realistic Large Bayesian Networks by Tiling. FLAIRS 2006: 592-597 - 2005
- [j7]Alexander R. Statnikov, Constantin F. Aliferis, Ioannis Tsamardinos, Douglas P. Hardin, Shawn Levy:
A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinform. 21(5): 631-643 (2005) - [j6]Alexander R. Statnikov, Ioannis Tsamardinos, Yerbolat Dosbayev, Constantin F. Aliferis:
GEMS: A system for automated cancer diagnosis and biomarker discovery from microarray gene expression data. Int. J. Medical Informatics 74(7-8): 491-503 (2005) - [j5]Yindalon Aphinyanaphongs, Ioannis Tsamardinos, Alexander R. Statnikov, Douglas P. Hardin, Constantin F. Aliferis:
Research Paper: Text Categorization Models for High-Quality Article Retrieval in Internal Medicine. J. Am. Medical Informatics Assoc. 12(2): 207-216 (2005) - [c21]Laura E. Brown, Ioannis Tsamardinos, Constantin F. Aliferis:
A Comparison of Novel and State-of-the-Art Polynomial Bayesian Network Learning Algorithms. AAAI 2005: 739-745 - [c20]Alexander R. Statnikov, Ioannis Tsamardinos, Constantin F. Aliferis:
Using the GEMS System for Cancer Diagnosis and Biomarker Discovery from Microarray Gene Expression Data. AAAI 2005: 1710-1711 - [c19]Lawrence D. Fu, Ioannis Tsamardinos:
A Comparison of Bayesian Network Learning Algorithms from Continuous Data. AMIA 2005 - [c18]Alexander R. Statnikov, Ioannis Tsamardinos, Constantin F. Aliferis:
Using the GEMS System for Supervised Analysis of Cancer Microarray Gene Expression Data. AMIA 2005 - 2004
- [c17]Douglas P. Hardin, Ioannis Tsamardinos, Constantin F. Aliferis:
A theoretical characterization of linear SVM-based feature selection. ICML 2004 - [c16]Laura E. Brown, Ioannis Tsamardinos, Constantin F. Aliferis:
A Novel Algorithm for Scalable and Accurate Bayesian Network Learning. MedInfo 2004: 711-715 - [c15]Alexander R. Statnikov, Constantin F. Aliferis, Ioannis Tsamardinos:
Methods for Multi-Category Cancer Diagnosis from Gene Expression Data: A Comprehensive Evaluation to Inform Decision Support System Development. MedInfo 2004: 813-817 - 2003
- [j4]Ioannis Tsamardinos, Martha E. Pollack:
Efficient solution techniques for disjunctive temporal reasoning problems. Artif. Intell. 151(1-2): 43-89 (2003) - [j3]Ioannis Tsamardinos, Thierry Vidal, Martha E. Pollack:
CTP: A New Constraint-Based Formalism for Conditional, Temporal Planning. Constraints An Int. J. 8(4): 365-388 (2003) - [j2]Martha E. Pollack, Laura E. Brown, Dirk Colbry, Colleen E. McCarthy, Cheryl Orosz, Bart Peintner, Sailesh Ramakrishnan, Ioannis Tsamardinos:
Autominder: an intelligent cognitive orthotic system for people with memory impairment. Robotics Auton. Syst. 44(3-4): 273-282 (2003) - [c14]Ioannis Tsamardinos, Constantin F. Aliferis:
Towards Principled Feature Selection: Relevancy, Filters and Wrappers. AISTATS 2003: 300-307 - [c13]Constantin F. Aliferis, Ioannis Tsamardinos, Alexander R. Statnikov:
HITON: A Novel Markov Blanket Algorithm for Optimal Variable Selection. AMIA 2003 - [c12]Constantin F. Aliferis, Ioannis Tsamardinos, Pierre P. Massion, Alexander R. Statnikov, Nafeh Fananapazir, Douglas P. Hardin:
Machine Learning Models for Classification of Lung Cancer and Selection of Genomic Markers Using Array Gene Expression Data. FLAIRS 2003: 67-71 - [c11]Ioannis Tsamardinos, Constantin F. Aliferis, Alexander R. Statnikov:
Algorithms for Large Scale Markov Blanket Discovery. FLAIRS 2003: 376-381 - [c10]Lewis J. Frey, Douglas H. Fisher, Ioannis Tsamardinos, Constantin F. Aliferis, Alexander R. Statnikov:
Identifying Markov Blankets with Decision Tree Induction. ICDM 2003: 59-66 - [c9]Ioannis Tsamardinos, Constantin F. Aliferis, Alexander R. Statnikov:
Time and sample efficient discovery of Markov blankets and direct causal relations. KDD 2003: 673-678 - [c8]Constantin F. Aliferis, Ioannis Tsamardinos, Pierre P. Massion, Alexander R. Statnikov, Douglas P. Hardin:
Why Classification Models Using Array Gene Expression Data Perform So Well: A Preliminary Investigation of Explanatory Factors. METMBS 2003: 47-53 - [c7]Constantin F. Aliferis, Ioannis Tsamardinos, Alexander R. Statnikov, Laura E. Brown:
Causal Explorer: A Causal Probabilistic Network Learning Toolkit for Biomedical Discovery. METMBS 2003: 371-376 - 2002
- [c6]Alan Berfield, Panos K. Chrysanthis, Ioannis Tsamardinos, Martha E. Pollack, Sujata Banerjee:
A Scheme for Integrating e-Services in Establishing Virtual Enterprises. RIDE 2002: 134-142 - [c5]Ioannis Tsamardinos:
A Probabilistic Approach to Robust Execution of Temporal Plans with Uncertainty. SETN 2002: 97-108 - 2001
- [c4]Martha E. Pollack, Colleen E. McCarthy, Sailesh Ramakrishnan, Ioannis Tsamardinos:
Execution-Time Plan Management for a Cognitive Orthotic System. Advances in Plan-Based Control of Robotic Agents 2001: 179-192 - 2000
- [c3]Ioannis Tsamardinos, Martha E. Pollack, John F. Horty:
Merging Plans with Quantitative Temporal Constraints, Temporally Extended Actions, and Conditional Branches. AIPS 2000: 264-272
1990 – 1999
- 1998
- [j1]Cristina Bicchieri, Martha E. Pollack, Carlo Rovelli, Ioannis Tsamardinos:
The potential for the evolution of co-operation among web agents. Int. J. Hum. Comput. Stud. 48(1): 9-29 (1998) - [c2]Ioannis Tsamardinos, Nicola Muscettola, Paul H. Morris:
Fast Transformation of Temporal Plans for Efficient Execution. AAAI/IAAI 1998: 254-261 - [c1]Nicola Muscettola, Paul H. Morris, Ioannis Tsamardinos:
Reformulating Temporal Plans for Efficient Execution. KR 1998: 444-452
Coauthor Index
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