Loading...


Photos



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.

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, William Cushing, and Subbarao Kambhampati, "State Agnostic Planning Graphs: Deterministic, Non-Deterministic, and Probabilistic Planning", Artificial Intelligence, Volume 175, pages 848-889, 2011. [ps]
  • Daniel Bryce, "Wumpus World in Introductory Artificial Intelligence", ACM Journal of Computing in Small Colleges, Volume 27, Issue 2, pages 60-66, December 2011. [pdf]
  • Daniel Bryce, "Planning with Multiple Prefences versus Planning with No Preference", ISRN Artificial Intelligence, Volume 2012, pages 1-9, 2012. [pdf]
  • Daniel Bryce, Michael Verdicchio, and Seungchan Kim, "Planning Interventions in Biological Systems", ACM Transactions on Intelligent Systems and Technology: Special Issue on Applications of Automated Planning, Volume 1, Issue 2, Paper 11, pages 1-26, 2010. [pdf]
  • 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, 2010.

    Conferences
  • Daniel Bryce and Christopher Weber, "Planning and Acting in Incomplete Domains", ICAPS, 2011. [pdf] [pptx] [video]
  • Roger Mailler, Daniel Bryce, Jiaying Shen, and Ciaran Oreilly, "MABLE: A Modular Architecture For Bootstrapped Learning", AAMAS, 2009. [pdf] [talk ppt] [poster pdf]
  • Daniel Bryce and Seungchan Kim, "Planning for Gene Regulatory Network Intervention", IJCAI, 2007. [pdf] [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
  • A. Olsen and D. Bryce. "Randward and Lamar: Randomizing the FF Heuristic", 2011 International Planning Competition, 2011. [pdf]
  • J. Robertson and D. Bryce. "Reachability Heuristics for Planning in Incomplete Domains", ICAPS'09 Workshop on Heuristics for Domain Independent Planning, 2009. [pdf] [ppt]
  • C. Morrison, D. Bryce, I. Fasel, and A. Rebguns. "Augmenting Instructable Computing with Planning Technology", ICAPS'09 Workshop on the International Competition for Knowledge Engineering in Planning and Scheduling, 2009. [pdf][pptx]
  • 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]
  • Daniel Bryce, "Planning in Incomplete Domains", USU CS TR-11-001, March 2011. [pdf]











































































Projects


  • "Controlling a Modular Architecture for Bootstrapped Learning Experiments", DARPA Contract HR001-07-C-0060, SRI International Subcontract 27-001321, DARPA Bootstrapped Learning. The Bootstrapped Learning project is aimed at building a digital student that can be taught complex concepts through multiple laddered lessons. As PI, I am researching strategies for selecting between learning algorithms to learn concepts taught by natural instruction methods. Award Total: $450,138. Term: 8/2008-11/2011.





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.
  • DeFault 1.0 [jar] Planner and domains for ICAPS'11 paper.
  • POND is now hosted on Sourceforge
  • DeFault is now hosted at Github





Presentations


  • "Scalable Planning Under Uncertainty" ICAPS 2009 Best Disseratation Award. [ppt][mov]
  • "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 09)
  • CS5600: Problem Solving and Expert Systems (Summer and Fall 09)
  • CS6600: Advanced Intelligent Systems (Spring 09)




Students


  • Jared Robertson (MS)
  • Alan Olsen (MS)
  • Christopher Weber (MS)
  • Dan Morwood (PhD)



Tutorials





6th International Planning Competition

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

Visitors

Visitor Map