News

2/1: AIJ McLUG article appears

2/10: AAAI-08 Reviewing starts

4/1: Stanford CS227 teaching begins

4/1: ICAPS Uncertainty Workshop Proposal Accepted

8/1: USU Webpage created

8/26: Running the IPC experiments!

8/26: ICAPS Uncertainty Workshop Program Finalized

9/12: Down Under at ICAPS

 

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Welcome

Welcome to my webpage! Below you can find information about me, including research interests, papers, software, presentations, and more. Everything is on this page and you can use the links on the left to skip to the right place.

If you are a student looking to work on one of my projects (or do research in AI), then send me a note about your interests with a resume or CV. I have open research positions to be filled immediately.

About Me

My research is in the field of AI and spans the topics of planning, Markov decision processes, heuristic search, multi-criteria decision making, and knowledge-based learning. My current projects include Bootstrapped Learning (field programmable intelligent systems through natural instruction), basic research on automated planning (planning with uncertainty), and applications of AI to systems biology (intervention planning).

Papers


    Journals
  • Daniel Bryce, Subbarao Kambhampati, and David E. Smith, "Sequential Monte Carlo in Reachability Heuristics for Probabilistic Planning", Artificial Intelligence, Volume 172/6-7, pages 685-715 , 2008. [pdf] [ps]
  • Daniel Bryce and Subbarao Kambhampati, "A Tutorial on Planning Graph Based Reachability Heuristics", AI Magazine, Volume 28, Number 1 (Spring 2007), 2007. [pdf]
  • Daniel Bryce, Subbarao Kambhampati, and David E. Smith, "Planning Graph Heuristics for Belief Space Search", Journal of Artificial Intelligence Research, Volume 26, pages 35-99, 2006. [pdf] [html] [domains] [Linux Binary]

    Dissertation
  • Daniel Bryce, "Scalable Planning Under Uncertainty", Arizona State Univeristy, Department of Computer Science and Engineering, May 2007.[pdf]

    Book Chapters
  • Daniel Bryce and Seungchan Kim, "Planning Interventions for Gene Regulatory Networks as Partially Observable Markov Decision Processes", in (Eds. Sanjoy Das, Doina Caragea, W. H. Hsu, and Stephen M. Welch) Computational Methodologies in Gene Regulatory Networks.

    Conferences
  • Daniel Bryce and Seungchan Kim, "Planning for Gene Regulatory Network Intervention", IJCAI, 2007. [pdf] [supplementary materials] [ppt]
  • Daniel Bryce, Subbarao Kambhampati, and David E. Smith,"Sequential Monte Carlo for Probabilistic Planning Reachability Heuristics", ICAPS, 2006. (Best Paper Nomination) [pdf] [domains]
  • William Cushing and Daniel Bryce, "State Agnostic Planning Graphs: and their application to belief space planning", AAAI 2005. (Best Paper Nomination) [pdf] [domains] [POND 1.1 Linux Binary] [slides] [supporting materials] [audio from talk]
  • Daniel Bryce, and Subbarao Kambhampati, "Cost Sensitive Reachability Heuristics for Handling State Uncertainty", UAI, 2005.[pdf] [Linux Binary] [domains] [slides]
  • Daniel Bryce and Subbarao Kambhampati, "Heuristic Guidance Measures for Conformant Planning", ICAPS 2004.[pdf] [slides]

    Workshops, Posters, and Doctoral Consortia
  • D. Bryce, W. Cushing, and S. Kambhampati. "Model-Lite Planning: Diverse Multi-Option Plans & Dynamic Objective Functions", 3rd Workshop on Planning and Plan Execution for Real-World Systems: Principles and Practices for Planning in Execution (held at ICAPS'07), 2007.[pdf][ppt][wav]
  • D. Bryce and S. Kim. "Planning for Gene Regulatory Network Intervention", 2nd IEEE/NLM International Workshop on Life Science Systems and Applications, 2006 (LSSA 2006).[pdf]
  • D. Bryce and D.E. Smith. "Using Correlation to Compute Better Probability Estimates in Plan Graphs", ICAPS 2006 Workshop on Planning Under Uncertainty and Execution Control for Autonomous Systems, 2006. [pdf]
  • D. Bryce. "Sequential Monte Carlo In Probabilistic Planning Reachability Heuristics", ICAPS 2006 Doctoral Consortium. [pdf]
  • D. Bryce. "POND: The Partially-Observable and Non-Deterministic Planner", ICAPS 2006 Notes on The 5th International Planning Competition, 2006. [pdf]
  • D. Bryce and S. Kambhampati. "Cost Sensitive Conditional Planning", ICAPS 2005 Poster Session. [pdf]
  • D. Bryce. "Scaling Decision Theoretic Planning", ICAPS 2005 Doctoral Consortium. [pdf]
  • D. Bryce. "Planning Graph Heuristics for Incomplete and Non-Deterministic Domains", ICAPS 2004 Doctoral Consortium. [pdf]
  • Daniel Bryce, Subbarao Kambhampati, and David E. Smith, "Planning in Belief Space with a Labelled Uncertainty Graph", AAAI 2004 Workshop on Learning and Planning in Markov Processes -- Advances and Challenges. [pdf]
  • Daniel Bryce and Subbarao Kambhampati, "Heuristic Guidance Measures for Conformant Planning", ICAPS 2003 workshop on Planning under Uncertainty and Incomplete Information. [pdf]

    Technical Reports
  • Daniel Bryce, William Cushing, and Subbarao Kambhampati, "Probabilistic Planning is Multiobjective!", ASU CSE TR-07-006, June 2007. [pdf]




















































Projects


  • Bootstrapped Learning (8/08-10/10, DARPA IPTO): "Control of a Modular Architecture for Bootstrapped Learning Experiments (MABLE)". This project focusses on control of an agent that learns from natural instruction. I am researching the meta-control aspects of using multiple learning algorithms to do bootstrapped learning.




Software


  • POND 2.0 [tgz] This is the version taking part in the IPC5 conformant planning track.
  • POND 2.1 [tgz] This is the version used in ICAPS-06 and TR version of McLUG work.
  • POND 2.2 [tgz] Some bug fixes and a new search algorithm.




Presentations


  • "Model-Lite Planning: Diverse Multi-option plans & Dynamic Objective Functions". ICAPS 2007 Workshop on Planning and Plan Execution for Real World Systems. [ppt]
  • "Planning Interventions for Gene Regulatory Networks", with Seungchan Kim, at IJCAI, 2007 [ppt]
  • "Sequential Monte Carlo in Probabilistic Planning Reachability Heuristics". Honeywell Labs, 2006 [ppt]
  • "Planning for Gene Regulatory Network Intervention". LSSA-06 [ppt]
  • "Sequential Monte Carlo in Probabilistic Planning Reachability Heuristics". ICAPS-06. [ppt]
  • "Sequential Monte Carlo in Probabilistic Planning Reachability Heuristics". ICAPS-06 Doctoral Consortium Poster. [ppt][pdf]
  • "Scalable Planning Under Uncertainty". ARCS Reception 2006. [ppt]
  • "Planning Interventions for Gene Regulatory Networks". TGEN Retreat 2006. [ppt]
  • "Scalable Planning Under Uncertainty". AI Lunch Seminar. ASU. 3/7/06. [ppt]
  • "Cost Based Reachability Heuristics for Handling State Uncertainty". UAI-05 [ppt]
  • "State Agnostic Planning Graphs". By Will Cushing. AAAI-05 [ppt]
  • "Planning Graph Heuristics for Conformant Planning". ICAPS-04 [ppt]
















Teaching


  • CS227 Reasoning Methods in Artificial Intelligence. (At Stanford, Spring Quarter 2008, w/ Neil Yorke-Smith.)
  • CS6890 ST: Decision Making in AI. (USU, Spring Semester 2008)



Tutorials





6th International Planning Competition

I am co-organizing the uncertainty tracks of the 6th IPC with Olivier Buffett. A wiki for the competition is here.