Peer-Reviewed Papers (In Chronological Order)
- Masataro Asai, Akihiro Kishimoto, Adi Botea, Radu Marinescu, Elizabeth Daly M, and Spyros Kotoulas. 2017. Efficient Optimal Search under Expensive Edge Cost Computation. In Proc. International Joint Conference on Artificial Intellifence(IJCAI)
- Asai, M.; Fukunaga, A: 2017. “Exploration Among and Within Plateaus in Greedy Best-First Search”. In ICAPS2017. pdf
- Asai, M.; Fukunaga, A: 2017. “Tie-Breaking Strategies for Cost-Optimal Best First Search.” Journal of Artificial Intelligence Research 58 (2017): 67-121. pdf
- Asai, M.; Fukunaga, A: 2016. Tiebreaking Strategies for A* Search: How to Explore the Final Frontier. In AAAI-2016. pdf presentation poster. (Also submitted to Japanese Society for Artificial Intelligence, received JSAI Annual Conference Student Incentive Award)
- Asai, M.; Fukunaga, A: 2015. Solving Large-Scale Planning Problems by Decomposition and Macro Generation. In ICAPS2015. pdf . CAP planner is available at https://github.com/guicho271828/CAP .
- Asai, M.; Fukunaga, A: 2014. Fully Automated Cyclic Planning for Large-Scale Manufacturing Domains. In ICAPS2014. pdf poster presentation
Presentation can be moved forward/backward with N/P key. For the further help and usage, click here.
- Asai, M.; Fukunaga, A: 2017. Classical Planning in Deep Latent Space: From Unlabeled Images to PDDL (and back). Knowledge Engineering for Planning and Scheduling (KEPS) Workshop (ICAPS2017).
- Asai, M.; Fukunaga, A: 2017. Tie-Breaking in A* as Satisficing Search. Heuristics and Search for Domain-independent Planning (HSDIP) Workshop (ICAPS2017).
- Asai, M.: 2017. Exploiting Search Space Structure in Classical Planning: Analyses and Algorithms (Dissertation Abstract, 2017 version). (ICAPS2017 Doctorial Consortium). pdf
- Asai, M.: 2016. Exploiting Search Space Structure in Classical Planning: Analyses and Algorithms (Dissertation Abstract). (ICAPS2016 Doctorial Consortium). pdf
- Endo, S; Asai, M.; Fukunaga, A: 2014. Evaluation of a Simple, Window-based, Replanning Approach to Plan Optimization. Heuristics and Search for Domain-independent Planning (HSDIP) Workshop (ICAPS2016). pdf presentation
- Asai, M.; Fukunaga, A: 2014. Applying Problem Decomposition to Extremely Large
Planning Domains. Knowledge Engineering for Planning and Scheduling (KEPS) Workshop
(ICAPS2014). pdf (
submitted version–> final version) poster presentation
See my github repo for the latest activity!
- CAP – Component Abstraction Planner, which decompose the given problem,
solve each subproblem, make the subplans into macros and then plans in an
enhanced problem with those macros
- 1.5 coverage in large domains!
- Higer coverage in ipc2011 learning track, without learning time!
- PDDL – A Common Lisp library to read/write/analyse PDDL files. It has
- a PDDL reader / parser
- CLOS-based object oriented interface to analyse each objects
- various useful accessors for objects e.g. predicate/propositions/action/types
- methods like
(ground-action action objects)
- a pretty formatter
- a simulator (STRIPS and action-costs are supported)
- CELL-ASSEMBLY – The PDDL files and the explanation of a CELL-ASSEMBLY
manufacturing domain, which appears at [Asai, Fukunaga ICAPS2014]
- Is currently enlisted as one of “Real and Realistic Planning Domains” by Patrik Haslum
- aaai-template – For org-mode lovers and reserchers of artificial intelligence. A
set of scripts and templates for faster publishing of papers with AAAI
- converting SVG images to grayscale pngs (I recommend Inkscape as an editor)
- typeset the TeX files with pdflatex
- checks for any Overfull hbox
- checks for paper limit
- Combination of simple scripts
make-periodicallywould be useful. It waits for the changes, and if a change is detected, it runs
make, then notify the result via
inotify(pop up inteface available in gtk).