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2020 – today
- 2024
[j35]Adrian Ly, Richard Dazeley
, Peter Vamplew, Francisco Cruz, Sunil Aryal:
Elastic step DQN: A novel multi-step algorithm to alleviate overestimation in Deep Q-Networks. Neurocomputing 576: 127170 (2024)- [j34]Weijia Wang, Wei Pan, Chaofan Dai, Richard Dazeley, Lei Wei, Bernard Rolfe, Xuequan Lu:
Segmentation-driven feature-preserving mesh denoising. Vis. Comput. 40(9): 6201-6217 (2024) - [c32]Peter Vamplew, Cameron Foale, Conor F. Hayes, Patrick Mannion, Enda Howley, Richard Dazeley, Scott Johnson, Johan Källström, Gabriel de Oliveira Ramos, Roxana Radulescu, Willem Röpke, Diederik M. Roijers:
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning. AAMAS 2024: 2717-2721 - [c31]Sheldon Fung, Wei Pan, Xiao Liu, John Yearwood, Richard Dazeley, Xuequan Lu:
TopFormer: Topology-Aware Transformer for Point Cloud Registration. CVM (1) 2024: 112-128 - [i29]Kewen Ding, Peter Vamplew, Cameron Foale, Richard Dazeley:
An Empirical Investigation of Value-Based Multi-objective Reinforcement Learning for Stochastic Environments. CoRR abs/2401.03163 (2024) - [i28]Peter Vamplew, Cameron Foale, Conor F. Hayes, Patrick Mannion, Enda Howley, Richard Dazeley, Scott Johnson, Johan Källström, Gabriel de Oliveira Ramos, Roxana Radulescu, Willem Röpke, Diederik M. Roijers:
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning. CoRR abs/2402.02665 (2024) - [i27]Peter Vamplew, Cameron Foale, Richard Dazeley:
Value function interference and greedy action selection in value-based multi-objective reinforcement learning. CoRR abs/2402.06266 (2024) - [i26]Dhiraj Neupane, Mohamed Reda Bouadjenek, Richard Dazeley, Sunil Aryal:
Data-driven Machinery Fault Detection: A Comprehensive Review. CoRR abs/2405.18843 (2024) - [i25]Xin Hao, Bahareh Nakisa, Mohammad Naim Rastgoo, Richard Dazeley:
IReCa: Intrinsic Reward-enhanced Context-aware Reinforcement Learning for Human-AI Coordination. CoRR abs/2408.07877 (2024) - [i24]Lakpa Dorje Tamang, Mohamed Reda Bouadjenek, Richard Dazeley, Sunil Aryal:
Margin-bounded Confidence Scores for Out-of-Distribution Detection. CoRR abs/2410.07185 (2024) - [i23]Peter Vamplew, Conor F. Hayes, Cameron Foale, Richard Dazeley, Hadassah Harland:
Multi-objective Reinforcement Learning: A Tool for Pluralistic Alignment. CoRR abs/2410.11221 (2024) - [i22]Hadassah Harland, Richard Dazeley, Peter Vamplew, Hashini Senaratne, Bahareh Nakisa, Francisco Cruz:
Adaptive Alignment: Dynamic Preference Adjustments via Multi-Objective Reinforcement Learning for Pluralistic AI. CoRR abs/2410.23630 (2024) - 2023
- [j33]Adam Bignold, Francisco Cruz, Matthew E. Taylor, Tim Brys, Richard Dazeley, Peter Vamplew, Cameron Foale:
A conceptual framework for externally-influenced agents: an assisted reinforcement learning review. J. Ambient Intell. Humaniz. Comput. 14(4): 3621-3644 (2023) - [j32]Francisco Cruz, Thommen George Karimpanal, Miguel A. Solis, Pablo Barros, Richard Dazeley:
Human-aligned reinforcement learning for autonomous agents and robots. Neural Comput. Appl. 35(23): 16689-16691 (2023) - [j31]Richard Dazeley, Peter Vamplew, Francisco Cruz:
Explainable reinforcement learning for broad-XAI: a conceptual framework and survey. Neural Comput. Appl. 35(23): 16893-16916 (2023) - [j30]Hadassah Harland, Richard Dazeley, Bahareh Nakisa, Francisco Cruz, Peter Vamplew:
AI apology: interactive multi-objective reinforcement learning for human-aligned AI. Neural Comput. Appl. 35(23): 16917-16930 (2023) - [j29]Francisco Cruz, Richard Dazeley, Peter Vamplew, Ithan Moreira:
Explainable robotic systems: understanding goal-driven actions in a reinforcement learning scenario. Neural Comput. Appl. 35(25): 18113-18130 (2023) - [j28]Adam Bignold, Francisco Cruz, Richard Dazeley, Peter Vamplew, Cameron Foale:
Human engagement providing evaluative and informative advice for interactive reinforcement learning. Neural Comput. Appl. 35(25): 18215-18230 (2023) - [j27]Adam Bignold, Francisco Cruz, Richard Dazeley, Peter Vamplew, Cameron Foale:
Persistent rule-based interactive reinforcement learning. Neural Comput. Appl. 35(32): 23411-23428 (2023) - [j26]Zafaryab Rasool, Sunil Aryal, Mohamed Reda Bouadjenek, Richard Dazeley:
Overcoming weaknesses of density peak clustering using a data-dependent similarity measure. Pattern Recognit. 137: 109287 (2023) - [j25]Hung Son Nguyen, Francisco Cruz, Richard Dazeley:
Towards a Broad-Persistent Advising Approach for Deep Interactive Reinforcement Learning in Robotic Environments. Sensors 23(5): 2681 (2023) - [c30]Peter Vamplew, Benjamin J. Smith, Johan Källström, Gabriel de Oliveira Ramos, Roxana Radulescu, Diederik M. Roijers, Conor F. Hayes, Friedrik Hentz, Patrick Mannion, Pieter J. K. Libin, Richard Dazeley, Cameron Foale:
Scalar Reward is Not Enough. AAMAS 2023: 839-841 - [c29]Conor F. Hayes, Roxana Radulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa M. Zintgraf, Richard Dazeley, Fredrik Heintz, Enda Howley, Athirai A. Irissappane, Patrick Mannion, Ann Nowé, Gabriel de Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers:
A Brief Guide to Multi-Objective Reinforcement Learning and Planning. AAMAS 2023: 1988-1990 - [c28]Supriya Roy, Bahareh Nakisa, Pubudu N. Pathirana, Richard Dazeley:
A Wearable Multi-Sensor Fusion Approach for Gender Recognition based on Deep Learning. ICBRA 2023: 114-119 - [c27]Weijia Wang, Xuequan Lu, Di Shao, Xiao Liu, Richard Dazeley, Antonio Robles-Kelly, Wei Pan:
Weighted Point Cloud Normal Estimation. ICME 2023: 2015-2020 - [c26]Adrian Ly, Richard Dazeley, Peter Vamplew, Francisco Cruz, Sunil Aryal:
Elastic step DDPG: Multi-step reinforcement learning for improved sample efficiency. IJCNN 2023: 1-6 - [i21]Weijia Wang, Xuequan Lu, Di Shao, Xiao Liu, Richard Dazeley, Antonio Robles-Kelly, Wei Pan:
Weighted Point Cloud Normal Estimation. CoRR abs/2305.04007 (2023) - [i20]Jianhua Li, Sophie McKenzie, Richard Dazeley, Frank Jiang, Keshav Sood:
Current Status and Trends of Engineering Entrepreneurship Education in Australian Universities. CoRR abs/2308.06943 (2023) - 2022
- [j24]Conor F. Hayes, Roxana Radulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa M. Zintgraf, Richard Dazeley, Fredrik Heintz, Enda Howley, Athirai A. Irissappane, Patrick Mannion, Ann Nowé, Gabriel de Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers:
A practical guide to multi-objective reinforcement learning and planning. Auton. Agents Multi Agent Syst. 36(1): 26 (2022) - [j23]Peter Vamplew, Benjamin J. Smith, Johan Källström, Gabriel de Oliveira Ramos, Roxana Radulescu, Diederik M. Roijers, Conor F. Hayes, Fredrik Heintz, Patrick Mannion, Pieter J. K. Libin, Richard Dazeley, Cameron Foale:
Scalar reward is not enough: a response to Silver, Singh, Precup and Sutton (2021). Auton. Agents Multi Agent Syst. 36(2): 41 (2022) - [j22]Thai Doan Chuong, Vicky H. Mak-Hau, John Yearwood, Richard Dazeley, M. T. Nguyen, T. Cao:
Robust Pareto solutions for convex quadratic multiobjective optimization problems under data uncertainty. Ann. Oper. Res. 319(2): 1533-1564 (2022) - [j21]Budi Kurniawan, Peter Vamplew, Michael Papasimeon, Richard Dazeley, Cameron Foale:
Discrete-to-deep reinforcement learning methods. Neural Comput. Appl. 34(3): 1713-1733 (2022) - [j20]Peter Vamplew, Cameron Foale, Richard Dazeley:
The impact of environmental stochasticity on value-based multiobjective reinforcement learning. Neural Comput. Appl. 34(3): 1783-1799 (2022) - [c25]Francisco Cruz, Charlotte Young, Richard Dazeley, Peter Vamplew:
Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios. IROS 2022: 894-901 - [i19]Jincen Jiang, Xuequan Lu, Lizhi Zhao, Richard Dazeley, Meili Wang:
Masked Autoencoders in 3D Point Cloud Representation Learning. CoRR abs/2207.01545 (2022) - [i18]Francisco Cruz, Charlotte Young, Richard Dazeley, Peter Vamplew:
Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios. CoRR abs/2207.03214 (2022) - [i17]Adrian Ly, Richard Dazeley, Peter Vamplew, Francisco Cruz, Sunil Aryal:
Elastic Step DQN: A novel multi-step algorithm to alleviate overestimation in Deep QNetworks. CoRR abs/2210.03325 (2022) - [i16]Francisco Cruz, Adam Bignold, Hung Son Nguyen, Richard Dazeley, Peter Vamplew:
Broad-persistent Advice for Interactive Reinforcement Learning Scenarios. CoRR abs/2210.05187 (2022) - 2021
- [j19]Cristian Millán-Arias, Bruno J. T. Fernandes, Francisco Cruz, Richard Dazeley, Sérgio Fernandes:
A Robust Approach for Continuous Interactive Actor-Critic Algorithms. IEEE Access 9: 104242-104260 (2021) - [j18]Richard Dazeley, Peter Vamplew, Cameron Foale, Charlotte Young, Sunil Aryal, Francisco Cruz:
Levels of explainable artificial intelligence for human-aligned conversational explanations. Artif. Intell. 299: 103525 (2021) - [j17]Peter Vamplew, Cameron Foale, Richard Dazeley, Adam Bignold:
Potential-based multiobjective reinforcement learning approaches to low-impact agents for AI safety. Eng. Appl. Artif. Intell. 100: 104186 (2021) - [j16]Sunil Aryal, KC Santosh, Richard Dazeley:
usfAD: a robust anomaly detector based on unsupervised stochastic forest. Int. J. Mach. Learn. Cybern. 12(4): 1137-1150 (2021) - [j15]Ngoc Duy Nguyen, Thanh Thi Nguyen, Peter Vamplew, Richard Dazeley, Saeid Nahavandi:
A Prioritized objective actor-critic method for deep reinforcement learning. Neural Comput. Appl. 33(16): 10335-10349 (2021) - [c24]Goodger Nikolaj, Peter Vamplew, Cameron Foale, Richard Dazeley:
Language Representations for Generalization in Reinforcement Learning. ACML 2021: 390-405 - [i15]Adam Bignold, Francisco Cruz, Richard Dazeley, Peter Vamplew, Cameron Foale:
Persistent Rule-based Interactive Reinforcement Learning. CoRR abs/2102.02441 (2021) - [i14]Conor F. Hayes, Roxana Radulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa M. Zintgraf, Richard Dazeley, Fredrik Heintz, Enda Howley, Athirai A. Irissappane, Patrick Mannion, Ann Nowé, Gabriel de Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers:
A Practical Guide to Multi-Objective Reinforcement Learning and Planning. CoRR abs/2103.09568 (2021) - [i13]Richard Dazeley, Peter Vamplew, Cameron Foale, Charlotte Young, Sunil Aryal, Francisco Cruz:
Levels of explainable artificial intelligence for human-aligned conversational explanations. CoRR abs/2107.03178 (2021) - [i12]Angel Ayala, Francisco Cruz, Bruno J. T. Fernandes, Richard Dazeley:
Explainable Deep Reinforcement Learning Using Introspection in a Non-episodic Task. CoRR abs/2108.08911 (2021) - [i11]Richard Dazeley, Peter Vamplew, Francisco Cruz:
Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey. CoRR abs/2108.09003 (2021) - [i10]Hung Son Nguyen, Francisco Cruz, Richard Dazeley:
A Broad-persistent Advising Approach for Deep Interactive Reinforcement Learning in Robotic Environments. CoRR abs/2110.08003 (2021) - [i9]Peter Vamplew, Benjamin J. Smith, Johan Källström, Gabriel de Oliveira Ramos, Roxana Radulescu, Diederik M. Roijers, Conor F. Hayes, Fredrik Heintz, Patrick Mannion, Pieter J. K. Libin, Richard Dazeley, Cameron Foale:
Scalar reward is not enough: A response to Silver, Singh, Precup and Sutton (2021). CoRR abs/2112.15422 (2021) - 2020
- [j14]Thanh Thi Nguyen, Ngoc Duy Nguyen, Peter Vamplew, Saeid Nahavandi, Richard Dazeley, Chee Peng Lim:
A multi-objective deep reinforcement learning framework. Eng. Appl. Artif. Intell. 96: 103915 (2020) - [c23]Cristian Millán-Arias, Bruno J. T. Fernandes, Francisco Cruz, Richard Dazeley, Sérgio Fernandes:
A Robust Approach for Continuous Interactive Reinforcement Learning. HAI 2020: 278-280 - [c22]Angel Ayala, Francisco Cruz, Diego Campos, Rodrigo Rubio, Bruno J. T. Fernandes, Richard Dazeley:
A Comparison of Humanoid Robot Simulators: A Quantitative Approach. ICDL-EPIROB 2020: 1-6 - [i8]Peter Vamplew, Cameron Foale, Richard Dazeley:
A Demonstration of Issues with Value-Based Multiobjective Reinforcement Learning Under Stochastic State Transitions. CoRR abs/2004.06277 (2020) - [i7]Budi Kurniawan, Peter Vamplew, Michael Papasimeon, Richard Dazeley, Cameron Foale:
Discrete-to-Deep Supervised Policy Learning. CoRR abs/2005.02057 (2020) - [i6]Francisco Cruz, Richard Dazeley, Peter Vamplew:
Explainable robotic systems: Interpreting outcome-focused actions in a reinforcement learning scenario. CoRR abs/2006.13615 (2020) - [i5]Adam Bignold, Francisco Cruz, Matthew E. Taylor, Tim Brys, Richard Dazeley, Peter Vamplew, Cameron Foale:
A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review. CoRR abs/2007.01544 (2020) - [i4]Ithan Moreira, Javier Rivas, Francisco Cruz, Richard Dazeley, Angel Ayala, Bruno J. T. Fernandes:
Deep Reinforcement Learning with Interactive Feedback in a Human-Robot Environment. CoRR abs/2007.03363 (2020) - [i3]Angel Ayala, Francisco Cruz, Diego Campos, Rodrigo Rubio, Bruno J. T. Fernandes, Richard Dazeley:
A Comparison of Humanoid Robot Simulators: A Quantitative Approach. CoRR abs/2008.04627 (2020) - [i2]Adam Bignold, Francisco Cruz, Richard Dazeley, Peter Vamplew, Cameron Foale:
Human Engagement Providing Evaluative and Informative Advice for Interactive Reinforcement Learning. CoRR abs/2009.09575 (2020)
2010 – 2019
- 2019
- [c21]Budi Kurniawan, Peter Vamplew, Michael Papasimeon, Richard Dazeley, Cameron Foale:
An Empirical Study of Reward Structures for Actor-Critic Reinforcement Learning in Air Combat Manoeuvring Simulation. Australasian Conference on Artificial Intelligence 2019: 54-65 - [c20]Francisco Cruz, Richard Dazeley, Peter Vamplew:
Memory-Based Explainable Reinforcement Learning. Australasian Conference on Artificial Intelligence 2019: 66-77 - 2018
- [j13]Peter Vamplew, Richard Dazeley, Cameron Foale, Sally Firmin, Jane Mummery:
Human-aligned artificial intelligence is a multiobjective problem. Ethics Inf. Technol. 20(1): 27-40 (2018) - [j12]Peter Vamplew, Richard Dazeley, Cameron Foale, Tanveer A. Choudhury:
Non-functional regression: A new challenge for neural networks. Neurocomputing 314: 326-335 (2018) - [c19]Omaru Maruatona, Peter Vamplew, Richard Dazeley, Paul A. Watters:
Rapid Anomaly Detection Using Integrated Prudence Analysis (IPA). PAKDD (Workshops) 2018: 137-141 - 2017
- [j11]Peter Vamplew, Rustam Issabekov, Richard Dazeley, Cameron Foale, Adam Berry, Tim Moore, Douglas C. Creighton:
Steering approaches to Pareto-optimal multiobjective reinforcement learning. Neurocomputing 263: 26-38 (2017) - [j10]Peter Vamplew, Richard Dazeley, Cameron Foale:
Softmax exploration strategies for multiobjective reinforcement learning. Neurocomputing 263: 74-86 (2017) - [c18]Omaru Maruatona, Peter Vamplew, Richard Dazeley, Paul A. Watters:
Evaluating Accuracy in Prudence Analysis for Cyber Security. ICONIP (5) 2017: 407-417 - 2015
- [j9]Robert Layton, Paul A. Watters, Richard Dazeley:
Authorship analysis of aliases: Does topic influence accuracy? Nat. Lang. Eng. 21(4): 497-518 (2015) - [c17]Peter Vamplew, Rustam Issabekov, Richard Dazeley, Cameron Foale:
Reinforcement Learning of Pareto-Optimal Multiobjective Policies Using Steering. Australasian Conference on Artificial Intelligence 2015: 596-608 - 2014
- [i1]Diederik Marijn Roijers, Peter Vamplew, Shimon Whiteson, Richard Dazeley:
A Survey of Multi-Objective Sequential Decision-Making. CoRR abs/1402.0590 (2014) - 2013
- [j8]Diederik M. Roijers, Peter Vamplew, Shimon Whiteson, Richard Dazeley:
A Survey of Multi-Objective Sequential Decision-Making. J. Artif. Intell. Res. 48: 67-113 (2013) - [j7]Robert Layton, Paul Andrew Watters, Richard Dazeley:
Automated unsupervised authorship analysis using evidence accumulation clustering. Nat. Lang. Eng. 19(1): 95-120 (2013) - [j6]Robert Layton, Paul Andrew Watters, Richard Dazeley:
Evaluating authorship distance methods using the positive Silhouette coefficient. Nat. Lang. Eng. 19(4): 517-535 (2013) - [c16]Robert Layton, Paul A. Watters, Richard Dazeley:
Local n-grams for Author Identification Notebook for PAN at CLEF 2013. CLEF (Working Notes) 2013 - 2012
- [j5]Robert Layton, Paul Andrew Watters, Richard Dazeley:
Recentred local profiles for authorship attribution. Nat. Lang. Eng. 18(3): 293-312 (2012) - [c15]Robert Layton, Paul A. Watters, Richard Dazeley:
Unsupervised authorship analysis of phishing webpages. ISCIT 2012: 1104-1109 - [c14]Andrei V. Kelarev, Richard Dazeley, Andrew Stranieri, John Yearwood, Herbert F. Jelinek:
Detection of CAN by Ensemble Classifiers Based on Ripple Down Rules. PKAW 2012: 147-159 - [c13]Omaru Maruatona, Peter Vamplew, Richard Dazeley:
RM and RDM, a Preliminary Evaluation of Two Prudent RDR Techniques. PKAW 2012: 188-194 - 2011
- [j4]Richard Dazeley, Sung Sik Park, Byeong Ho Kang:
Online knowledge validation with prudence analysis in a document management application. Expert Syst. Appl. 38(9): 10959-10965 (2011) - [j3]Paul Andrew Watters, Robert Layton, Richard Dazeley:
How much material on BitTorrent is infringing content? A case study. Inf. Secur. Tech. Rep. 16(2): 79-87 (2011) - [j2]Peter Vamplew, Richard Dazeley, Adam Berry, Rustam Issabekov, Evan Dekker:
Empirical evaluation methods for multiobjective reinforcement learning algorithms. Mach. Learn. 84(1-2): 51-80 (2011) - [c12]Ather Saeed, Andrew Stranieri, Richard Dazeley:
Fault-tolerant data aggregation scheme for monitoring of critical events in grid based healthcare sensor networks. SpringSim (HPC) 2011: 56-64 - 2010
- [c11]Robert Layton, Paul A. Watters, Richard Dazeley:
Automatically determining phishing campaigns using the USCAP methodology. eCrime 2010: 1-8 - [c10]Richard Dazeley, Philip Warner, Scott Johnson, Peter Vamplew:
The Ballarat Incremental Knowledge Engine. PKAW 2010: 195-207 - [c9]Richard Dazeley, John Yearwood, Byeong Ho Kang, Andrei V. Kelarev:
Consensus Clustering and Supervised Classification for Profiling Phishing Emails in Internet Commerce Security. PKAW 2010: 235-246
2000 – 2009
- 2009
- [c8]Peter Vamplew, Richard Dazeley, Ewan Barker, Andrei V. Kelarev:
Constructing Stochastic Mixture Policies for Episodic Multiobjective Reinforcement Learning Tasks. Australasian Conference on Artificial Intelligence 2009: 340-349 - 2008
- [j1]Richard Dazeley, Byeong Ho Kang:
Epistemological Approach to the Process of Practice. Minds Mach. 18(4): 547-567 (2008) - [c7]Peter Vamplew, John Yearwood, Richard Dazeley, Adam Berry:
On the Limitations of Scalarisation for Multi-objective Reinforcement Learning of Pareto Fronts. Australasian Conference on Artificial Intelligence 2008: 372-378 - [c6]Richard Dazeley, Byeong Ho Kang:
An Approach for Generalising Symbolic Knowledge. Australasian Conference on Artificial Intelligence 2008: 379-385 - [c5]Richard Dazeley, Byeong Ho Kang:
Detecting the Knowledge Boundary with Prudence Analysis. Australasian Conference on Artificial Intelligence 2008: 482-488 - [c4]Richard Dazeley, Byeong Ho Kang:
Generalising Symbolic Knowledge in Online Classification and Prediction. PKAW 2008: 91-108 - 2004
- [c3]Richard Dazeley, Byeong Ho Kang:
An Augmentation Hybrid System for Document Classification and Rating. PRICAI 2004: 985-986 - 2003
- [c2]Richard Dazeley, Byeong Ho Kang:
Weighted MCRDR: Deriving Information about Relationships between Classifications in MCRDR. Australian Conference on Artificial Intelligence 2003: 245-255 - [c1]Richard Dazeley, Byeong Ho Kang:
Rated MCRDR: Finding non-Linear Relationships Between Classifications in MCRDR. HIS 2003: 499-508
Coauthor Index
Diederik M. Roijers
aka: Diederik Marijn Roijers
aka: Diederik Marijn Roijers
[j35] [c32] [i29] [i28] [i27] [i23] [i22] [j33] [j31] [j30] [j29] [j28] [j27] [c30] [c29] [c26] [j24] [j23] [j21] [j20] [c25] [i18] [i17] [i16] [j18] [j17] [j15] [c24] [i15] [i14] [i13] [i11] [i9] [j14] [i8] [i7] [i6] [i5] [i2] [c21] [c20] [j13] [j12] [c19] [j11] [j10] [c18] [c17] [i1] [j8] [c13] [j2] [c10] [c8] [c7]
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