8. atkinson - aoard information sciences
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David J. Atkinson, Ph.DProgram Manager
AFOSR/AOARDAir Force Research Laboratory
AFOSR
AOARD: INFORMATION SCIENCES
AFOSR: ROBUST COMPUTATIONALINTELLIGENCE
14 March 2011
Distribution A: Approved for public release; distribution is unlimited. 88ABW-2011-0772
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2011 AFOSR SPRING REVIEWAOARD INFORMATION SCIENCES
NAME: David J. Atkinson, Ph.D (Program Manager)TEAM: Prof. Hiroshi Motoda (Senior Scientific Advisor)Peter Friedland, Ph.D (Senior Scientific Advisor)
DESCRIPTION OF PORTFOLIO:
Information Sciencesin Asia. Covers theoretical and experimental workin computer science and intelligent systems aligned with needs ofAFOSR/RSL programs, AFRL technical directorate challenges, andemerging topics of special significance
SUB-AREAS: Intelligent systems(machine-learning, human-computer interaction,computer sensing and perception, sensor networks, automated reasoning)
Trusted Systems(cyber-security, formal software verification)
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2011 AFOSR SPRING REVIEWAFOSR ROBUST COMPUTATIONAL INTELLIGENCE
NAME: David J. Atkinson, Ph.D (Program Manager)TEAM: Peter Friedland, Ph.D (Senior Scientific Advisor)
DESCRIPTION OF PORTFOLIO:
Theoretical and experimental work in artificial intelligence and related
disciplines focused on creating robust intelligent autonomous systemsthat are able to operate effectively: in novel situations despite gaps,conflicts and ambiguities in knowledge; that learn, adapt and improve withexperience; and that function at a level of flexibility and generalitycomparable to that of humans and animals
SUB-AREAS:
Knowledge representation, cognitive architectures, automated reasoning,machine learning and adaptation, meta-cognition, human-machineinteraction
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Challenging and Exciting ScienceAOARD Information Sciences
Machine understanding of implicit human intention
Who: K. Kosuge (JAPAN), S.Y. Lee (KOREA), F. Chen (AUSTRALIA)
Impact: Enable highly adaptive, intuitive and responsive human-computer, human-robot interaction in high cognitive workload tasks
Formal verification of very large and complex software codes
Who: G. Klein, G. Heiser (AUSTRALIA) Impacts:
Automated verification and eventually self-verifying codes
Enable enormous reduction in software lifecycle costs and reduced operationalrisk from residual software defects
Enable large-scale provably-correct integration of formal logic systems fortrustworthy autonomous systems
Harden VPN traffic against statistical fingerprinting techniques
Who: K. Anagnostakis (SINGAPORE)
Impact: Enable adaptive traffic concealment methods to prevent attacks
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Transformational CapabilityAFOSR Robust Computational Intelligence
Autonomous SystemsThe ability of a system, without human intervention, to compose and selectamong different courses of action to accomplish goals based on its knowledgeand understanding of the world and the exigencies of the moment.
IMPACTS
Potential enormous
increases in capabilities
Significant time-domainoperational advantages, i.e.,
operational tempo
Manpower efficiencies and
cost reductions
--Technology Horizons 2010 Report
CAPABILITIES
Robust and Trustworthy - the system will functioncorrectly, i.e., as designed and as we intend, despite
a real world that is messy, ambiguous, dynamicandadversarialincluding the risk of system faults
Cognitive software system architectures bring
together multiple specialized AI components
Problem-solving using integrated heterogeneousreasoning techniques
Flexible autonomy via human-machine interaction;machines as partners, not simply tools
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Non-Robust / Robust
Doesnt learn from experience
Hard failure
Constantly learning and adapting
Stays on mission despite near-crippling system failures
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RCI Program Engagement withOther Research Organizations
Our Niche: Emphasis on systems approach to achieving significant
improvements in robustness of intelligent systems
National Science Foundation
Briefed to CISE/IIS PMs during formulation with continuing discussions
US multi-agency information exchange in AI, Robotics
Facilitating international cooperative research for NSF with Japan and Korea
Office of Naval Research
Briefed to C4ISR PMs during formulation with continuing discussions
Supporting formulation of new ONR program in Machine Reasoning
DASA, Army Research Laboratory / TARDEC Shared direction of research grants, provided programmatic and technical support
to international robotics challenge jointly sponsored with DSTO (Australia)
Briefed ARL Chief Scientist for Robotics, branch chief and other personnel duringformulation of RCI; continuing discussions.
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Recent TransitionsAOARD Information Sciences
Formal System Verification for Trustworthy Embedded Systems
Researching methods for formally integrating heterogeneous logic systems inapplication area of model-based software verification
PI: Dr. Gerwin Klein, National ICT Australia (NICTA), Sydney, AU
Partner: Steven Drager, AFRL/RITB, co-funding, host PI visit, guiding future directions
Neuroergonomic Research for Online Assessment of Cognitive Workload
Research on the integration and selection of robust cognitive workload measuressuch as speech signals and EEG
PI: Dr. Fang Chen, National ICT Australia (NICTA), Sydney, AU
Partner: Dr. James Christensen, AFRL/RHCP, co-funding, hosted PI visit, developingCooperative Research Agreement to aid integration of research studies, methods, tools, data
Multi-Agent Sensor Network Systems Characterizing UAV sensor network robustness to loss of nodes and links
PI: Prof. Brian Anderson, Australia National University, Canberra, AU
Industry: Facilitated introduction to Boeing R&TD Center (Seattle) which now has separatecontract with Prof. Anderson to mature research on UAV sensor formation control
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Recent Transitions (2)AOARD Information Sciences
Sensor Data Integrity and Mitigation of Perceptual Failures Researching methods for automatic detection and mitigation of faults and uncertainty in sensor
data and machine perception
PI: Dr. Thierry Peynot, ARC Center for Autonomous Systems, University of Sydney, Sydney, AU
Partner: Brian Skibba, AFRL/RX, strong advocacy, research data to be used to help refinerobotic sensor requirements
Multi-Autonomous Ground-robotic International Challenge (MAGIC 2010)
Jointly sponsored by DARPA, ARL, ONRG, AOARD and Australia DSTO
Research, develop and demonstrate a multi-vehicle robotic team to survey, map,
recognize and respond to threats in a dynamic urban environment AOARD issued and jointly directed multiple research grants to 10 teams and
contributed technical expertise to the governing technical team.
AFRL/RX (B. Skibba) participated on leadership team and as a judge
The challenge demonstration was held in Adelaide, November 2010.
AOARD jointly directing follow-on grants to the top three research teams.
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Research Examples
AOARD Information Sciences Measuring and Ensuring Performance and Information Quality in Multi-Agent
Sensor Network Systems
Prof. Brian Anderson, Australia National University and National ICT Australia
AFOSR Robust Computational Intelligence
Extending Semantic and Episodic Memory to Support Robust Decision Making
Prof. John Laird, University of Michigan
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Measuring and Ensuring Performance and InformationQuality in Multi-Agent Sensor Network Systems
PI: Prof. Brian Anderson, Australian National University and NICTA, Canberra, AU
Scientific Objective:
Characterize a sensor networks ability to continue performing given a lossof psensing agents and/or qcommunication links
Key performance aspect is localizability: Localizability is the ability to determine the position of all nodes given certain internode
distances and the absolute position of a limited number of anchor nodes)
Scientific/Technical Approach:
Build on a graph theory characterization of sensor network localizabilityusing graph rigidity analysis
Breakthrough Opportunity:
Network tolerance to enemy attacks and jamming.
Network tolerance to loss of multiple sensors and links due to powerdegradation etc.
Results are applicable to other network properties (e.g., formation control)
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Insight: Localizability Based OnA Test for Graph Rigidity
Two types of nodes: anchors and (ordinary) sensors.
Anchor positions known; certain internodedistances are measured - typically betweenclose nodes (unit disk model).
Localization is the task of finding theposition of all nodes given the measurements.
The characterization of localizable networks is given in terms of graphtheoretical properties of the network topology. A network is localizable if itis globally rigidand there are three non collinear anchor nodesin the network.
Rigid network = intuitive notion: if one builtthe network with bars and joints then theresulting framework would not flex
Redundant Rigidity = remains rigid afterremoval of any edge
Global Rigidity = redundant rigidity + 3-connectedness
MINIMALLY RIGID NONRIGID
NONRIGIDRIGID, BUT NOT
MINIMALLY SO; this isredundant rigidity
a b a b
cda
d cc
ba
d
cd
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Anderson - Progress
A set of combinatorial conditions was found for robustness to node loss.
This is the first timeanyone has considered localization robustness to multiplenode/link losses (the general case).
Showed that it is sufficientfor a network to have certain connectivity conditionsinorder to be robust against node loss.
There is a small threshold for the transmission range of nodesbeyond which the
network will obtain the desired robustness against the loss of nodes.
Several minimal link count structures which are robustly localizable tothe loss of up to 2 nodes are proposed for the first time (general claim fornnodes is future research).
Surprising Discovery: The energy required for having adesignated large percentage of nodes (e.g. 99%) connectedis asymptotically and vanishingly smallcomparedwith that required for having a 100% connected network!=> In a large scale network the overallperformance may improve significantly by leaving fewhard-to-reach nodes out of robustness requirements.
S
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Extending Semantic and Episodic Memory toSupport Robust Decision Making
PI: Prof. John Laird, University of Michigan
Scientific Objective:
Develop a general, robust cognitive architecture that can be used as thebasis for creating artificial agents
Effectively use large databases of knowledge for reasoning
Learning diverse knowledge from experience
Scientific/Technical Approach: Within the context of the existing Soar architecture
New algorithms for semantic and episodic memory functionality, while alsomeeting real-time performance requirements
Investigate functional and algorithmic synergies of semantic and episodic
memory in support of problem-solving reasoning
Breakthrough opportunity:
Artificial agents that perform integrated diverse forms of reasoning with largesources of experiential knowledgeand massive existing databases
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Soar Cognitive ArchitectureBackground
Soar embodies a theory of cognitive computationbased on goals, problem spaces, states andoperators (one of several theories and architectures; another is ACT-R)
Uses a wide variety of knowledge representations(procedural, declarative, episodic)
Data-driven decision-elaboration-action cycletransforms existing state into the goal state
Implements a wide variety of reasoning techniques
Uses heuristicsand other methods to proceed in ambiguous situations
Long TermMemories
Active Memory& Reasoning
Perception &Action
Learning &Remembering
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Laird - Progress
Real-Time Performance
Characterized new alternative algorithms forworking memory, semantic memory andepisodic memory in robot test apparatus at
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Portfolio Topic OverviewAOARD Information Sciences
Autonomous systems Machine-learning
Human-computer interaction
Computer sensing and perception Automated reasoning
Autonomous mobile sensor networks
Trusted systems Cyber-security
Formal software verification
P tf li T i O i
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Portfolio Topic OverviewAFOSR Robust Computational Intelligence
Knowledge representation Cognitive architectures
Automated reasoning
Machine learning Meta-cognition
Human-machine interaction
Cognitive robotics
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Wrap-up
Thank you very much for your attention!
Contact Info (as of 1 April 2011)
David J. AtkinsonInstitute for Human and Machine [email protected]
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Backup
Projects, PIs and Institutions Conferences and Workshops
Recent Publications
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Projects
Understanding how to build long-lived learning collaboratorsPI: Prof. Kenneth Forbus, Northwestern University, Contact: [email protected]
"Intelligence in the Now: Robust Intelligence in Complex DomainsPI: Prof. Leslie Kaebling, MIT; Co-I: Prof. Tomas Lozano-Perez, MIT Contact: [email protected]
Extending Semantic and Episodic Memory to Support Robust Decision Making
PI: Prof. John Laird, University of Michigan, Contact: [email protected]
"A Unified Architectural Approach to the Hybrid Mixed Challenge of Situational Assessment and Prediction
Prof. Paul Rosenbloom, University of Southern California, Contact: [email protected]
"Contextual Awareness for Robust Robot Autonomy
Dr. Reid Simmons, Carnegie Mellon University, Contact: [email protected]
"Robust Multi-Agent Sensor Network Systems
Prof. Brian D. O. Anderson, NICTA-Canberra and Australia National University, Contact: [email protected]
Integrating Logical and non-Logical ReasoningPI: Prof Maurice Pagnucco, University of New South Wales, Contact: [email protected]
"Sensor Data Integrity and Mitigation of Perceptual Failures
Dr. Thierry Peynot, University of Sydney; Co-I: Dr. Hugh Durrant-Whyte, NICTA (CEO), Contact: [email protected]
Learning Within Optimization
PI: Prof Toby Walsh, NICTA Sydney, Contact: [email protected]
"Human-Robot Interaction: Intention Recognition and Mutual EntrainmentProf. Kazuhiro Kosuge, Tohoku University, Contact: [email protected]
Machine Understanding of Implicit Human Intention
PI: Prof. Soo-Young Lee, Korea Advanced Institute of Science & Technology (KAIST), Contact: [email protected]
Formal System Verification for Trustworthy Embedded Systems Continuation
PI: Dr. Gerwin Klein, Co:I: Dr. Gernot Heiser, NICTA-Sydney, Contact: [email protected]
Hardening Encrypted VPNs against Statistical Fingerprinting Attacks
PI: Dr. Kostas Anagnostakis, Niometrics Pte Ltd, Contact: [email protected]
Automatic Multimodal Cognitive Load Measurement
PI: Dr. Fang Chen, NICTA Sydney, Contact: [email protected]
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected] -
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Projects (Cont.)
Discovering hidden causal structure in data
PI: Prof. Takashi Washio, Osaka University, Contact: [email protected]
A density ratio approach to machine learningPI: Prof. Masashi Sugiyama, Tokyo Institute of Technology, Contact: [email protected]
Mass estimation and its applications
PI: Dr. Kai Ming Ting, Monash University, Contact: [email protected]
Security Protocol Verification and Optimization by Epistemic Model Checking
PI: Prof. Ronald van der Meyden, University of New South Wales, Contact: [email protected]
Transfer Learning for Adaptive Relation Extraction
PI: Dr. Hai-Leong Chieu, DSO National Laboratories, Contact: [email protected]
MAGIC 2010 The Virginia Tech Team
PI: Prof. Tomonari Furukawa, Virginia Polytechnic Institute and State University, Contact: [email protected]
MAGIC 2010 The Univ. Pennsylvania and BAE Systems TeamPI: Dr. Daniel D. Lee, University of Pennsylvania, Contact: [email protected]
MAGIC 2010 Competition - University of MichiganPI: Prof. Edwin Olson, University of Michigan, [email protected]
MAGIC 2010 - The Cornell TeamPI: Prof. Mark Campbell, Cornell University, Contact: [email protected]
MAGIC 2010 The Robotic Research TeamPI: Alberto Lacaze, Robotic Research LLC, Contact: [email protected]
MAGIC 2010 - The Strategic Engineering TeamPI: Richard Alpin, Strategic Engineering Pty Ltd., Contact: [email protected]
MAGIC 2010 - The Kingston TeamPI: Maj. Marc Fricker, The Royal Military College of Canada, Contact: [email protected]
MAGIC 2010 Competition - Flinders UniversityPI: Prof. David Powers, Flinders University, Contact: [email protected]
MAGIC 2010 The Chiba TeamPI: Mark Haley, Analytical Software Inc. Co-I: Prof. Kenzo Nonami, Chiba University, Contact: [email protected]
MAGIC 2010 The ASELSAN TeamPI: Faruk Menguc, ASELSAN A.S. Defence Systems , Contact: [email protected]
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected] -
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Conferences and Workshops
Annual Conference of the Prognostics and Health Management Society 2010, Portland, ORhttp://www.phmconference.org/
The Ninth International Workshop on the Algorithmic Foundations of Robotics (WAFR), Singapore,http://www.wafr.org/
The First International Conference on Future Generation Information Technology, Jeju, Korea,http://www.sersc.org/FGIT2009/
The 22nd Australasian Joint Conference on Artificial Intelligence, Melbourne, Australia,http://www.infotech.monash.edu.au/about/news/conferences/ai09/
9th ACM SIGGRAPH Intl Conf. on VR Continuum and Its Applications in Industry, Seoul, Korea,
http://www.vrcai2010.org/VSMM2010 (16th Intl Conf. on Virtual Systems and Multimedia), Seoul Korea, http://www.vsmm2010.or.kr/
PAKDD 2010: The 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Hyderabad, India,http://www.iiit.ac.in/conferences/pakdd2010/
PRICAI 2010: The 11th Pacific Rim International Conference on Artificial Intelligence, Daegu, Korea,http://www.pricai2010.org
The 10th IEEE International Conference on Data Mining (ICDM 2010), Sydney, Australia,
http://datamining.it.uts.edu.au/icdm10/
The 13th International Conference on Discovery Science, Canberra, Australia,http://www.cse.unsw.edu.au/~achim/DS10/
2010 IEEE-RIVF International Conference on Computing and Telecommunication Technologies (IEEE-RIVF 10),Hanoi, Vietnam, http://www.rivf.org
The Fifth International Conference on Knowledge, Information and Creativity Support Systems (KICSS 2010),Chiang Mai, Thailand, http://itpe.siit.tu.ac.th/kicss2010/ http://www.kicss2010.org
The Second Asian Conference on Machine Learning, Tokyo, Japan, http://sugiyama-www.cs.titech.ac.jp/ACML2010/
http://www.phmconference.org/http://www.wafr.org/http://www.sersc.org/FGIT2009/http://www.infotech.monash.edu.au/about/news/conferences/ai09/http://www.vrcai2010.org/http://www.vsmm2010.or.kr/http://www.iiit.ac.in/conferences/pakdd2010/http://www.pricai2010.org/http://datamining.it.uts.edu.au/icdm10/http://www.cse.unsw.edu.au/~achim/DS10/http://www.rivf.org/http://itpe.siit.tu.ac.th/kicss2010/http://www.kicss2010.org/http://sugiyama-www.cs.titech.ac.jp/ACML2010/http://sugiyama-www.cs.titech.ac.jp/ACML2010/http://sugiyama-www.cs.titech.ac.jp/ACML2010/http://sugiyama-www.cs.titech.ac.jp/ACML2010/http://www.kicss2010.org/http://itpe.siit.tu.ac.th/kicss2010/http://www.rivf.org/http://www.cse.unsw.edu.au/~achim/DS10/http://datamining.it.uts.edu.au/icdm10/http://www.pricai2010.org/http://www.iiit.ac.in/conferences/pakdd2010/http://www.vsmm2010.or.kr/http://www.vrcai2010.org/http://www.infotech.monash.edu.au/about/news/conferences/ai09/http://www.sersc.org/FGIT2009/http://www.wafr.org/http://www.phmconference.org/ -
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Recent Publications
Agangostakis, K. G. Portokalidis, P. Homburg, and H. Bos, Paranoid Android: Versatile Protection For Smartphones ACSAC10,http://www.cs.columbia.edu/~porto/Publications_files/paranoidandroid_acsac10.pdf
Al-Bataineh, O. and R. van der Meyden Epistemic Model Checking for Knowledge-Based Program Implementation: an Application toAnonymous Broadcast,, SecureComm'10, 6th International ICST Conference on Security and Privacy in Communication Networks,Singapore, Sept 7-9 2010.
Al-Bataineh, Omar I., Ron van der Meyden: Abstraction for Epistemic Model Checking of Dining Cryptographers-based Protocols CoRRabs/1010.2287: (2010), submitted for publication
Andronick ,J., From a proven correct microkernel to trustworthy large systems, Proc. 1st International Conference on Formal Verification ofObject-Oriented Software (FoVeOOS10), volume 6528 of LNCS, Springer-Verlag, Paris, France, June, 2010 Invited talk
Andronick J., and D. Greenaway and K. Elphinstone, Towards proving security in the presence of large untrusted components , Proc. 5thWorkshop on Systems Software Verification, Vancouver, Canada, October, 2010
Berthold, Timo, Thibaut Feydy and Peter J. Stuckey. Rapid Learning for Binary Programs, Integration of AI and OR Techniques in ConstraintProgramming for Combinatorial Optimization Problems, 7th International Conference, CPAIOR 2010, Bologna, Italy, June 14-18, 2010.
Bessiere Christian, and George Katsirelos, Nina Narodytska, Claude-Guy Quimper, Toby Walsh. Propagating Conjunctions of All DifferentConstraints. Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010, Atlanta,Georgia, USA, July 11-15, 2010.
Chen, S., Epps, J. and Chen, F., Eye Activity as a Measure of Human Mental Effort in HCI, Proc. International Conference on Intelligent
User Interfaces (IUI11), Palo, Alto, U.S.A., February 2011, to appear.
Derbinsky, N., Laird, J. E., Smith, B. Efficient fact retrieval from large semantic memories, Towards Efficiently Supporting Large Symbolic
Declarative Memories, ICCM 2010
Derbinsky, N., Laird, J. E., Efficient incorporation of environmental regularities to bias semantic retrievals, A Preliminary Functional Analysisof Memory in the Word Sense Disambiguation Task, AISB 2011 (submitted)
Heiser, G., and J. Andronick, K. Elphinstone, G. Klein, I. Kuz and L. Ryzhyk, The road to trustworthy systems, Proc. 5th Workshop onScalable Trusted Computing, Chicago, IL, USA, October, 2010 Invited paper
Huang, B., Yu, C., Anderson, B.D.O. and Mao, G., Connectivity-based Distance Estimation in Wireless Sensor Networks, IEEE Globecom
http://www.cs.columbia.edu/~porto/Publications_files/paranoidandroid_acsac10.pdfhttp://www.cs.columbia.edu/~porto/Publications_files/paranoidandroid_acsac10.pdf -
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Recent Publications (2)
Huang, B., Yu, C., Anderson, B.D.O., Analyzing error propagation in 1-D sensor network localization, IEEE Trans Aerospace and ElectronicSystems
Huang, B., Yu, C and Anderson, B.D.O., Estimating distances by connectivity in wireless sensor networks, Computer Networks
Kaelbling, Leslie Pack And Tomas Lozano-Perez. Hierarchical Task And Motion Planning In The Now, Accepted For IEEE InternationalConference On Robotics And Automaton, May 2011.
Klein, G., The L4.verified Projects - Next Steps ,Proc. 3rd International Conference on Verified Software: Theories, Tools, Experiments(VSTTE'10), volume 6217 of LNCS, pages 86-96, Springer 2010 -- Extended abstract, invited talk
Klein, G., From a Verified Kernel towards Verified Systems, Proc. 8th Asian Symposium on Programming Languages and Systems(APLAS'10), volume 62461 of LNCS, pages 21-33, Springer 2010. -- Extended abstract, invited talk
Klein, G., A Formally Verified OS Kernel. Now What?,Proc. 1st Intl. Conf. Interactive Theorem Proving (ITP'10), volume 6172 of LNCS,
pages 1-7, Springer 2010 -- Extended abstract, invited talk
Klein, G., and J. Andronick, K. Elphinstone, G. Heiser, D. Cock, P. Derrin, D. Elkaduwe, K. Engelhardt, R. Kolanski, M. Norrish, T. Sewell, H.Tuch and S. Winwood,, seL4: Formal verification of an OS kernel, Communications of the ACM, 53(6), 107115, (June, 2010)
Khawaja, M. A., Chen, F. and Marcus, N., Using Language Complexity to Measure Cognitive Load for Adaptive Interaction Design, Proc.
International Conference on Intelligent User Interfaces (IUI10), Hong Kong, China, February 2010, pp. 333-336.
Kuz, I., and G. Klein, C. Lewis and A. Walker, capDL: A language for describing capability-based systems, Proc. 1st Asia-Pacific Workshopon Systems, New Delhi, India, August, 2010
Laird, J. E., Derbinsky, N., Voigt, J. Large-scale performance evaluation of declarative memories in mobile robot domain, Performance
Evaluation of Declarative Memory Systems in Soar, BRIMS 2011 (submitted)
Le, P., Epps, J., Ambikairajah, E. and Sethu, V., Robust Speech-Based Cognitive Load Classification Using a Multi-band Approach, Proc.APSIPA Annual Summit and Conference (APSIPA 10), Biopolis, Singapore, December 2010, to appear.
Le, P., Epps, J., Choi, E. and Ambikairajah, E., A Study of Voice Source and Vocal ract Filter Based Features in Cognitive Load
Classification, Proc. International Conference on Pattern Recognition (ICPR10), Istanbul, Turkey, August 2010, pp. 4516-4519.
Liu, J., Morse, A.S., Anderson, B.D.O., Yu, C., and Mou, S., Deterministic gossiping with a request-based protocol, IFAC World Congress2011, Milano.
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Recent Publications (3)
Mao, G. and Anderson, B.D.O., On the asymptotic connectivity of random networks under the random connection model , IEEEInfocom 2011
Mao, G., Zhang, Z. and Anderson, B.D.O., Probability of k-hop connection under random connection model, IEEE Communications LettersMao, G. and Anderson, B.D.O., On the asymptotic distribution of the number of isolated nodes under the random connection model, IEEE
Transactions Information Theory
Melchior, N. A. and R. Simmons. Dimensionality Reduction for Trajectory Learning from Demonstration, In Proceedings of IEEE InternationalConference on Robotics and Automation, May 2010
Motevallian, S.A., Yu, C. and Anderson, B.D.O., Robustness to the loss of multiple nodes in the localization of sensor networks, IFAC WorldCongress 2011, Milano
Ng, S.C and Mao,G., Analysis of k-Hop Connectivity Probability in 2-D Wireless Networks with Infrastructure Support, IEEE Globecom
Ng,S.C. Zhang, W., Yang, Y. and Mao, G., Analysis of Access and Connectivity Probabilities in Vehicular Relay Networks, IEEE Journal onSelected Areas in Communications--Special Issue Vehicular Communications and Networks
Rosenbloom, P. S. (2010). Speculations on leveraging graphical models for architectural integration of visual representation and reasoning.Proceedings of the AAAI-10 Workshop on Visual Representations and Reasoning
Rosenbloom, P. S. (2010). Rethinking cognitive architecture via graphical models. Cognitive Systems Research. In press.
Rosenbloom, P. S. (2010). Combining procedural and declarative knowledge in a graphical architecture. Proceedings of the 10thInternational Conference on Cognitive Modeling (ICCM 2010).
Rosenbloom, P. S. (2010). Implementing first-order variables in a graphical cognitive architecture. Biologically Inspired Cognitive
Architectures: Proceedings of the First Annual Meeting of the BICA Society. Arlington, VA: IOS Press.
Rosenbloom, P. S. (2010). An architectural approach to statistical relational AI. Proceedings of the AAAI-10 Workshop on StatisticalRelational AI.
Rosenbloom, P. S. (2010). Towards a new generation of cognitive architectures. Proceedings of the 2nd International Conference onAdvanced Intelligence. Abstract only. Stuckey, Peter J.. Lazy clause generation: Combining the power of SAT and CP (and MIP?)solving, Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, 7th InternationalConference, CPAIOR 2010, Bologna, Italy, June 14-18, 2010.
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Recent Publications (4)
Ruiz, N., Chen, F. and Oviatt S., Multimodal Input, in Multimodal Signal Processing: Theory and Applications for Human-ComputerInteraction. Edited by Thiran, J.P., Marques, F. and Bourlard, H., Elsevier, 2010, Chapter 12, pp. 231-255.
Shames, I., Deghat, M and Anderson B.D.O., Safe formation control and coordination with obstacle avoidance, IFAC World Congress 2011,Milano.
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