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PH.D. CANDIDATE> ECE> CMU
TAOSF> RI> CMU
Teaching Asst (On Study Leave)> CS> NUS
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LOW, KIAN HSIANG BRYAN |劉謙雄| PH.D. CANDIDATE> ECE> CMU TAOSF> RI> CMU Teaching Asst (On Study Leave)> CS> NUS bryanlowATcsDOTcmuDOTedu | |||
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:: Conferences, Workshops & Special Issues [ robot ◊ agent ◊ ai ◊ ml ◊ algo ◊ sn ] __________| r o b o t |___________________________________________ ROBOTICS'08 ◊ 15 Jan ◊ 15 Apr ◊ 25-8 Jul ◊ ETH Zurich, Switzerland Topics Planning and Algorithms: Motion Planning, Mission Planning, Assembly Planning, Coordination, Complexity and Completeness, Field Application and Systems, Estimation and Machine Learning: Reinforcement Learning, Bayesian Techniques, Graphical Models, Mobile Robotics: Mapping, Localization, Collision Avoidance, Exploration, Mobile Robot Control, Distributed Systems: Sensor Networks, Multi-Robot Systems, Underwater Robotics, Aerial/Space Robotics ICRA'08 ◊ 14 Sep ◊ 7 Jan ◊ 19-23 May ◊ Pasadena, California Acceptance Rate 08=43.4% ◊ 07=44.0%(787/1787) ◊ 06=38.7%(680/1756) ◊ 05=45.4%(771/1700) ◊ 04=58.8%(858/1459) ◊ 03=60.5%(711/1176) ◊ 02=58.8%(689/1172) ◊ 01=64.5%(678/1051) ◊ 00=58.9%(641/1087) ◊ 99=62.2%(521/837) ◊ 98=62.0%(596/962) IROS'08 ◊ 22 Feb ◊ 6 Jun ◊ 22-6 Sep ◊ Nice, France Acceptance Rate 07=52.4% ◊ 06=46% ◊ 05=55.1%(663/1204) ◊ 04=55.3%(659/1192) ◊ 03=60.1%(626/1042) ◊ 02=60.2%(496/824) ◊ 01=58.2%(383/658) ◊ 00=72.6%(378/521) IAS-10'08 ◊ 15 Feb ◊ 1 Apr ◊ 23-5 Jul ◊ Baden-Baden, Germany Topics Aerial Vehicles, Actuators and Sensors, Automotive Robotics, Distributed Systems, Embodied Intelligence, Humanoid Robotics, Interactive Systems, Machine Learning and Adaptivity, Manipulation and Grasping, Medical Robotics Navigation, Perception, Planning, Prediction of Human Intention, Sensor Fusion, SLAM, Swarm Intelligence, Under Water Robotics Acceptance Rate 04=63.1+17.3%(106[Regular]+29[Short]/168) ◊ 02=67.6+11.3%(48[Regular]+8[Short]/71) DARS'08 ◊ 30 Jun ◊ 31 Aug ◊ 17-19 Nov ◊ Tsukuba, Japan Topics Architectures for teams of robots, Ambient Intelligence, Biologically inspired systems, Control issues in multi-robot systems, Distributed decision making/problem solving, Distributed/cooperative perception, Distributed planning, Distributed task execution, Human and robot interaction, Learning and adaptation in teams of robots, Multi-robot applications in exploration, search and rescue, service, etc Mobiligence (Emergence of Intelligence through Mobility), Modular robotics, Network robotics, Performance metrics for robot teams, Reconfigurable robots, Robot societies, Self-organizing robotic systems, Sensor networks, Swarm robotics, Task allocation AMAI Special Issue on Multi-Robot Coverage, Search, and Exploration ◊ 15 Aug ◊ 15 Nov ◊ 2008 Topics Research in multi robot area coverage, search, and exploration has been receiving consistent attention in recent years, due to the increasing number of real-world applications, such as vacuuming, lawn mowing, demining, surveillance, search and rescue operations, mapping, planetary exploration, etc. All of these applications require that the area of interest be covered by the robots sensors or end-effectors for various purposes. The use of multiple robots potentially provides redundancy and offers opportunities for increasing efficiency. The problem of multi robot area coverage imposes great challenges to researchers in robotics and AI area. This special issue will explore the new research frontiers that emerge as new applications are identified and new technologies in robots are introduced. The special issue follows in the footsteps of the highly successful 2001 AMAI special issue on coverage. We seek high-quality papers reporting on innovative work on coverage, search, and exploration and related areas of research. Our focus is on algorithmic and analytical approaches. Heuristic approaches should be supported by analysis and empirical evidence. Submissions extending previously published conference papers are acceptable, as long as the extensions are clearly explained. The following potential topics are of interest: Complete-coverage path planning for a group of robots in known and unknown environments; Multiple robot coordination in area coverage, exploration, and/or search; Issues in trade-off of tasks between teams members; how team members decide who performs which task; Fault-tolerance and robustness in multi robot area coverage; Performance metrics for coverage, exploration, and search of the use of heterogeneous teams (combining different vehicles/ end-effectors/ sensors); Novel applications of coverage, exploration, and search; The effect of the task complexity and of the environment on the design of the cooperative capabilities of multi-robot systems; Communications aspects between the robots during multi-robot area coverage; Repeated coverage and patrolling; On-line search for a path, navigation. ______________________________| a g e n t |_____________________ AAMAS-08 ◊ 23/26 Oct ◊ 19 Dec ◊ 12-6 May ◊ Estoril, Portugal Topics multiagent planning, multiagent learning, autonomy, robotics: cooperative perception, cooperative localization, middleware for multi-robot systems, multi-robot mapping and localization, networked robots, formal models of multi-robot plans. Acceptance Rate 08=22.2+15.0%(142[Paper]+96[Poster]/640) ◊ 07=22.8+25.0%(121[Paper]+133[Poster]/531) ◊ 06=23.1+24.5%(127[Paper]+135[Poster]/550) ◊ 05=24.1+23.0%(130[Paper]+124[Poster]/540) ◊ 04=24.5+23.6%(142[Paper]+137[Poster]/580) ◊ 03=24.7+32.2%(115[Paper]+150[Poster]/466) ◊ 02=26.8+24.9%(142[Paper]+132[Poster]/530) ◊ ICAA ◊ 01=21.4+24.6%(66[Paper]+76[Poster]/309) ◊ 00=24.0%(48[Paper]/200) ◊ 99=28.7%(43[Paper]/150) ◊ 98=31.0%(57[Paper]/184) ◊ 97=35.1%(59/168) ◊ ICMAS ◊ 00=19% ◊ 98=23% ◊ 96=28% ◊ 95=33% IAT-06 ◊ 5 Jul ◊ 4 Sep ◊ 18-22 Dec ◊ Hong Kong Topics Applications: Complex self-organized systems modeling and development, Hard computational domains, Physically embodied systems, Computational Models, Architecture, and Infrastructure: Agent and multi-agent infrastructure, Heterogeneity and interoperability, Models of perception, rationality, intention, emotion, coordination, action, and social behaviors, Multi-modal systems and interfaces, Scalability Autonomy-Oriented Computation (AOC) Paradigm: Adaptive computation, Autonomous societies, Complex behavior characterization and engineering, Emergent behavior, Self-organized intelligence Learning and Self-Adapting Agents: Adaptation and self-adaptation, Artificial life, Behavioral selection, Behavioral self-organization, Coordinating perception, thought, and action, Evolution and learning in dynamic environments, Integrated exploration and exploitation, Uncertainty management in multi-agent systems Data and Knowledge Management Agents: Human-agent interaction, Reasoning and planning Distributed Intelligence: Collective group behavior, Coordination and cooperation, Distributed intelligence, Dynamics of agent groups and populations, Effciency in distributed systems, Formal and computational models, Market-based computing, Social integration, Swarms Acceptance Rate 05=18+25%(305) ◊ 04=16+21%(45[Regular]+69[Short]/266) ◊ 03=23.6+20.7%(57[Regular]+50[Short]/242) ◊ 01=18.7+29.9%(25[Paper]+40[Poster]/134) ______________________________| a i |_____________________ AAAI-08 (Special Track on Physically Grounded AI) ◊ 25(abs)/30(paper) Jan ◊ 1 Apr ◊ 13-7 Jul ◊ Chicago, Illinois Topics AI for Robotics, Machine Learning applied to robotics, vision, and other activities grounded in the real world, Machine Learning for control and decision making, Motion planning, Sensor Networks Acceptance Rate 07=27.5%(253/921) ◊ 06=22+8.5%(236/774) ◊ 05=18.4+10.1%(148[Papers]+81[Posters]/803) ◊ 04=26.5%(120/453) ◊ 02=26%(121/469) ◊ 00=33%(143/431) ◊ 99=27%(109/400) ◊ 98=30%(144/475) ◊ 97=36%(117/323) ◊ 96=30%(197/643) ◊ 94=28%(222/780) ◊ 93=24%(126/524) ◊ 92=21%(133/636) ◊ 91=24%(142/603) ◊ 90=18%(161/892) ◊ 88=17%(148/850) ◊ 87=21%(149/715) ◊ 86=23%(187/817) UAI-08 ◊ 27(abs)/29(full) Feb ◊ 21 Apr ◊ 9-12 Jul ◊ Helsinki, Finland Topics theoretical or methodological advances in modeling, inference, learning and decision making under uncertainty; novel and insightful applications of these techniques within intelligent systems: multi-agent systems and sensor networks Acceptance Rate 07=32% ◊ 06=12+20%(68/213) ◊ 05=34.2%(83/243) ◊ 04=%(26[Paper]+50[Posters]/) ◊ 03=10.9+22.7%(25[Paper]+52[Posters]/229) ◊ 02=34% ◊ 01=40% ◊ 00=45% ◊ 98=45% ICAPS-06 ◊ 14 Nov ◊ 22 Dec ◊ 6-10 Jun ◊ Cumbria, UK Topics planning and scheduling theory, algorithms, and applications to areas such as manufacturing, space systems, disaster relief, software engineering, robotics, logistics, education, crisis response, and entertainment anytime planning and scheduling, applications of planning and scheduling, case-based planning, constraint reasoning for planning and scheduling, decision-theoretic planning and scheduling, deductive planning, distributed and multi-agent planning and scheduling, domain-independent classical planning, domain analysis and knowledge acquisition for planning and scheduling, dynamic scheduling, heuristic search-based planning, knowledge engineering techniques for planning and scheduling, mixed-initiative planning and scheduling, model-theoretic approaches to planning, planning and complexity, planning and execution, planning and learning, planning and perception, planning and reasoning about actions, planning and scheduling under uncertainty, planning and scheduling with complex domain models, planning and scheduling with complex objectives, planning with hierarchical task networks, planning with resources, planning, scheduling, and new information technology, plan validation and verification, reactive planning, real-time planning and scheduling, re-planning, robot planning, scalability in planning and scheduling, search, software development tools for planning and scheduling, Acceptance Rate 05=35% ◊ 04=31% ◊ 03=31%(30) ◊ 02=35.6%(32/90) ◊ 99=%(27) IAAI-06 ◊ 24 Jan ◊ 4 Apr ◊ 16-20 Jul ◊ Boston, MA Acceptance Rate 05=38%(18/48) ◊ 03=23% ◊ 1999=33.3%(18/54) ECAI-08 ◊ 25 Feb ◊ ◊ 21-5 Jul ◊ Patras, Greece Topics Adaptive Systems, Agents, AI Architectures, Automated Reasoning, Bayesian Learning, Cognitive Robotics, Constraint Programming, Constraint Satisfaction, Distributed AI, Human Computer Interaction, Machine Learning, Market-Oriented Programming, Model-Based Reasoning, Multi-Agent Systems, Neural Networks, Planning, Probabilistic Reasoning, Reasoning about Action and Change, Reinforcement Learning, Resource-Bounded Reasoning, Robotics, Scheduling, Spatial Reasoning, Uncertainty Acceptance Rate 06=26.1+13.6%(131[Paper]+75[Poster]/501+49) 04=22.9+12.1%(180[Paper]+95[Poster]/787) ◊ 02=27.4%(137/500) ◊ 00=32%(140/443) ◊ 98=35%(158/456) ◊ 96=30%(133/450) ◊ 94=33%(150/450) ◊ 92=29%(200/680) SAB'06 ◊ 30 Mar ◊ ◊ 25-30 Sep ◊ Rome, Italy Topics Action selection & behavioral sequencing, Navigation and mapping, Internal models and representation, Evolution, development and learning, Collective and social behavior, Emergent structures and behaviors, Neural correlates of behavior, Autonomous, bio-inspired, and hybrid robotics, Autonomous robotics, Applied adaptive behavior Acceptance Rate 02=24.1+26.3%(33[Paper]+36[Poster]/137) ◊ 00=36.7%(55/150) ◊ 92=56% IJCAI-07 ◊ 1 Jul ◊ 15 Sep ◊ 6-12 Jan ◊ Hyderabad, India Topics adaptive systems, AI architectures, applications, automated reasoning, autonomous agents, computational complexity, constraint programming, constraint satisfaction, decision theory, decision trees, distributed AI, enabling technologies, heuristics, human computer interaction, market-oriented programming, mathematical foundations, model-based reasoning, multiagent systems, negotiation, neural networks, nonclassical computation models, nonmonotonic reasoning, ontologies, planning, prediction, probabilistic reasoning, problem solving, reactive control, reasoning about actions and change, reinforcement learning, resource-bounded reasoning, robotics, scheduling, search, spatial reasoning, temporal reasoning, uncertainty Acceptance Rate 07=Oral 15.7% (252/1353), Poster 19.1% (258/1353) ◊ 05=Paper Track 18%(240/1333), Poster Track 21.8%(112/514) ◊ 03=Paper Track 20.7%(189/913 from full paper submissions), Poster Track 19.5%(63/322 from full paper submissions), Poster Track 21.6%(30/139 from poster submissions) ◊ 01=24.7%(197/796) ◊ 99=26%(194/750) ◊ 97=24%(216/882) ◊ 95=22%(249/1112) ◊ 93=25% ICTAI'05 Topics AI in Robotics, Reasoning Under Uncertainty, Machine Learning, Neural Networks, Planning and Scheduling, Hybrid Intelligent Systems Acceptance Rate 05=12.1%(36[Paper]/297) ◊ 03=27.3+27.3%(44[Paper]+44[Poster]/161) ◊ 02=32.1+28.6%(27[Paper]+24[Poster]/84) ◊ 01=51.2%(44[Paper]/86) PRICAI'06 Acceptance Rate 04=26.9+16.6%(96[Paper]+59[Poster]/356) ◊ 02=35.4+22.4%(57[Paper]+36[Poster]/161) ◊ 96=32% ___________________________________| m l |_____________________ MLJ Special Issue on ML in Space: Extending Our Reach ◊ 31 Mar ◊ 1 Aug Topics Machine learning can be used to significantly expand the capabilities of remote agents operating in space missions. For example, spacecraft could intelligently filter their observations to make the best use of available bandwidth or rovers with learning capabilities could more thoroughly and more quickly explore new environments. Autonomous robots can play a key role in creating a successful human presence on the Moon and Mars, both before humans arrive and in collaboration with them once humans are on site. However, care must be exercised in applying and developing techniques which will truly operate without human intervention. The risks and possible safety implications need to be well understood. The purpose of this special issue is to collect recent advances in machine learning for remote space or planetary environments and to identify novel space applications where machine learning could significantly increase capabilities, robustness, and/or efficiency. Key topics of interest include: - How to perform machine learning in a high-risk, remote environment - Learning with resource constraints (computation, memory, etc.) - Multi-mission machine learning - Novel applications and uses of machine learning in space - Methods that trade off exploration and exploitation, given mission science goals and safety/reliability requirements ICML-08 ◊ 8 Feb ◊ ◊ 5-9 Jul ◊ Helsinki, Finland Acceptance Rate 07=28.7%(150/522) ◊ 06=25.5%(140/548) ◊ 05=27.3%(134/491) ◊ 04=32.0%(118/368) ◊ 03=32.1%(119/371) ◊ 02=33.0%(86/261) ◊ 01=79[Papers] ◊ 00=43%(149/347) ◊ 99=35.5%(54/152) ◊ 98=35% ◊ 97=33% ◊ 94=22.7% NIPS'07 ◊ 8 Jun ◊ ◊ 4-7 Dec ◊ Vancouver, British Columbia, Canada Topics Algorithms and Architectures: statistical learning algorithms, neural networks, kernel methods, graphical models, Gaussian processes, dimensionality reduction and manifold learning, model selection, combinatorial optimization, active learning. Applications: innovative applications or fielded systems that use machine learning, including systems for time series prediction, bioinformatics, text/web analysis, multimedia processing, and robotics. Control and Reinforcement Learning: decision and control, exploration, planning, navigation, Markov decision processes, game-playing, multi-agent coordination, computational models of classical and operant conditioning. Learning Theory: generalization, regularization and model selection, Bayesian learning, spaces of functions and kernels, statistical physics of learning, online learning and competitive analysis, hardness of learning and approximations, large deviations and asymptotic analysis, information theory. Acceptance Rate 07=10.4+11.9% ◊ 06=24%(204/833) ◊ 05=25%(207/822) ◊ 04=7.9+25.2%(65[Oral+Spotlight]+207[Poster]/822) ◊ 03=30.0%(198/660) ◊ 02=31.1%(221/710) ◊ 01=30.2%(25[Paper]+171[Poster]/650) ◊ 99=32% ◊ 98=31% AISTATS-05 Acceptance Rate 03=15.0+27.0%(16[Paper]+28[Poster]/107) IJCNN'06 Acceptance Rate 04= (/785) >20% rejection ◊ 03=33.6%+46.6%(245[Papers]+340[Posters]/730) 15% rejection ◊ ICNN ◊ 97=48%[Papers]+20%[Posters] ◊ 93=39%[Papers] ECML'04 Acceptance Rate 05=11+8.7%(72/365) ◊ 04=18%(84[Paper]+19[Poster]/581) ◊ 03=24.1%(40[ECML]+40[PKDD]/332) ◊ 02=33%(/218)[53Joint,95ECML,70PKDD] ◊ 01=37.5%(90/240) ◊ 94=20% _____________________| a l g o |__________________________________ SODA-06 ◊ ◊ ◊ 22-24 Jan ◊ Miami, FL Topics Combinatorics and other aspects of Discrete Mathematics such as: Algebra, Combinatorial Structures, Discrete Optimization, Discrete Probability, Game Theory, Graph Drawing, Graphs and Networks, Mathematical Programming, Number Theory, Random Structures, Topological Problems. Other aspects of Computer Science such as: Combinatorial Scientific Computing, Communication Networks, Computational Geometry, Computer Graphics and Computer Vision, Computer Systems, Cryptography and Security, Data Compression, Databases and Information Retrieval, Distributed and Parallel Computing, Experimental Algorithmics, Internet Algorithmics, Machine Learning, On-line Problems, Quantum Computing, Pattern Matching, Robotics, Scheduling and Resource Allocation Problems, Symbolic Computation. Applications in the Sciences and Business such as: Biology, Physics, Manufacturing and Finance. Acceptance Rate 06=31.25%(135/432) ◊ 05=32.1%(126/392)[paper]+10.5%(10/95)[poster] STOC-05 ◊ ◊ ◊ 22-24 May ◊ Baltimore, MD Topics algorithms and data structures, complexity theory, cryptography, computational algebra and geometry, algorithmic graph theory, applications of logic, machine learning, parallel and distributed computing, theoretical aspects of databases, information retrieval, and networks, computational biology, quantum computation and other alternative models of computation. FOCS-05 ◊ ◊ ◊ 22-25 Oct ◊ Pittsburgh, PA APPROX-05 ◊ ◊ ◊ 22-24 Aug ◊ UC Berkeley Topics design and analysis of approximation algorithms, hardness of approximation, small space and data streaming algorithms, sub-linear time algorithms, embeddings and metric space methods, mathematical programming methods, coloring and partitioning, cuts and connectivity, geometric problems, game theory and applications, network design and routing, packing and covering, scheduling, other applications ALGO-06 ◊ ◊ ◊ 11-15 Sep ◊ ETH Zürich, Switzerland __________| s e n s o r n e t |____________________________ IPSN'06 ◊ 4 Nov ◊ 27 Jan ◊ 19-21 Apr ◊ Nashville, TN Topics Detection, classification, estimation, and tracking, Network coverage, connectivity, and longevity, Sensor tasking and control, Embedded architectures and tools, Energy and resource management, Distributed inference and fusion, Real-time scheduling, Simulation tools and environments, Networked sensing and control, Applications of sensor networks (e.g., automotive, battlefield, biology, construction, disaster recovery, environmental, medical, security) Acceptance Rate 05=20.6%(44/213) ◊ 04=34.5%(50/145) ◊ 03=64%(21[Paper]+23[Poster]/) SenSys'05 Topics Sensor network architecture and protocols, Distributed coordination algorithms (e.g., for localization, time synchronization, clustering, topology control, etc.), Distributed actuation and control, Analysis of real-world systems and fundamental limits, Applications Acceptance Rate 05=16.8%(21/125) ◊ 04=14.5+14.5%(21[Full]+21[Poster]/145) ◊ 03=18.5+19.2%(24[Full]+25[Poster]/130) :: AI, Robotics and Agent Societies
:: Official Journal Websites Robotics
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