Fault Aware Systems: Model-based Programming and Diagnosis Brian C. Williams 16.412J/6.834J March 8th, 2004 courtesy of JPL Four launches in 7 months MERS CSAIL

Mapping Topics: Topological Maps SLAM HMMs Revisited Additional reading: B. J. Kuipers Y.-T. Byun. 1991. \A robot exploration and mapping strategy based on a semantic hierarchy of spatial rep rstations. jour. s and

Model-based Diagnosis a failure is a discrepancy between the model and observations of an artifact a diagnosis restores consistency 1. Enumerate candidates in order of likelihood

Using the Forest to See the Trees: Context-based Object Recognition Bill Freeman Joint work with Antonio Torralba and Kevin Murphy Computer Science and Artificial Intelligence Laboratory MIT computer vision goal Recognize many different objects under many viewing conditions in unconstrained settings

Fast Solutions to CSPs Presented by: Robert Effinger Dan Lovell Presented To: 16.412J Cognitive Robotics MIT References: \Dynamic Backtracking\ Matthew L. Ginsberg, CIRL, University of Oregon Journal of Artificial Intelligence Research 1(1993)

Distributed constraint Satisfaction problems 2 Asynchronous algorithms Thomas leaute 16.412J-Cognitive Robotics april 7. 2004 Presentation outline Introduction to CSPs and DCSPs 2. The Asynchronous Backtracking Algorithm 3. The asynchronous Weak-Commitment Search algorithm 4. Conclusion and Introduction to the task Allocation problem M. Yokoo, E Durfee, T. Ishida and K. Kuwabara, Distributed Constraint Satisfa

Fast Solutions to CSp's Based on PROSSER, P. Hybrid algorithms for the constraint satisfaction problem. Computational Intelligence

Leslie Pack Kaelbling, Michael. Littman and Anthony R. Cassandra, \Planning and Acting in Partially Observable Stochastic Domains,\ Artificial Intelligence, Vol. 101, 1998. j. Pineau,g. Gordon&s. Thrun\Point-based- value iteration: An anytime algorithm for POMDPs\. International Joint Conference on Artificial Intelligence(JCAI) Acapulco, Mexico. Aug

Outline Lightning Summary Black Box Model of SIFT SLAM Vision System Challenges in Computer Vision What these challenges mean for visual SLAM How SIFT extracts candidate landmarks How landmarks are tracked in SIFT SLAM Alternative vision-based SLAM systems Open questions

Review: Constraint-based Interval Planning Simple Temporal Networks Schedulability and Scheduling Review: Temporal, Model-based Programming Managing Execution Uncertainty Execution with Dynamic Scheduling Execution with Explicit Models of Uncertainty