广义规划文献摘要

广义规划文献摘要¶

经典问题例子¶

Aminof, B., Giacomo, G. D., Murano, A., & Rubin, S. (2019). Planning under LTL environment specifications. In Proc. ICAPS, pp. 31-39.——积木世界 QNP 实例
Bonet, B., Frances, G., & Geffner, H. (2019). Learning features and abstract actions for computing generalized plans. In Proc. AAAI.——石头世界 \(Q_{clear}\) 与 Gripper 实例
Bonet, B., & Geffner, H. (2015). Policies that generalize: Solving many planning problems with the same policy. In IJCAI, pp. 2798-2804.——诸多经典问题

广义规划¶

Jimenez, S., Segovia-Aguas, J., & Jonsson, A. (2019). A review of generalized planning. The Knowledge Engineering Review, 34.（综述）
Aguas, J. S., Celorrio, S. J., & Jonsson, A. (2016). Generalized planning with procedural domain control knowledge. In Proc. ICAPS.
Belle, V., & Levesque, H. J. (2016). Foundations for generalized planning in unbounded stochastic domains. In KR, pp. 380-389.
Bercher, P., & Mattmuller, R. (2009). Solving non-deterministic planning problems with pattern database heuristics. In Proc. German Conf. on AI (KI), pp. 57-64. Springer.
Bonet, B., Palacios, H., & Geffner, H. (2009). Automatic derivation of memoryless policies and finite-state controllers using classical planners. In Proc. ICAPS-09, pp. 34-41.
Bonet, B., De Giacomo, G., Geffner, H., & Rubin, S. (2017). Generalized planning: Nondeterministic abstractions and trajectory constraints. In Proc. IJCAI.
Bonet, B., & Geffner, H. (2018). Features, projections, and representation change for generalized planning. In Proceedings of the 27^th International Joint Conference on Artificial Intelligence, pp. 4667-4673. AAAI Press.——将GP映射至QNP求解
Bonet, B., Palacios, H., & Geffner, H. (2009). Automatic derivation of memoryless policies and finite-state controllers using classical planners. In ICAPS.
Bueno, T. P., de Barros, L. N., Maua, D. D., & Sanner, S. (2019). Deep reactive policies for planning in stochastic nonlinear domains. In AAAI, Vol. 33, pp. 7530-7537.
Camacho, A., Bienvenu, M., & McIlraith, S. A. (2019). Towards a unified view of AI planning and reactive synthesis. In Proc. ICAPS, pp. 58-67.
Cimatti, A., Pistore, M., Roveri, M., & Traverso, P. (2003). Weak, strong, and strong cyclic planning via symbolic model checking. Artificial Intelligence, 147(1-2), 35-84.
Fikes, R., & Nilsson, N. (1971). STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 1, 27-120.（STRIPS规划语言的紧凑描述——规划语言）
Geffner, T., & Geffner, H. (2018). Compact policies for fully observable non-deterministic planning as SAT. In Proc. ICAPS.——将FOND问题转换为SAT问题求解
Hu, Y., & De Giacomo, G. (2011). Generalized planning: Synthesizing plans that work for multiple environments. In IJCAI, pp. 918-923.
Illanes, L., & McIlraith, S. A. (2019). Generalized planning via abstraction: arbitrary numbers of objects. In Proc. AAAI.
Martin, M., & Geffner, H. (2004). Learning generalized policies from planning examples using concept languages. Appl. Intelligence, 20(1), 9-19.
Muise, C. J., McIlraith, S. A., & Beck, C. (2012). Improved non-deterministic planning by exploiting state relevance. In Proc. ICAPS.

自动规划¶

Geffner, H., & Bonet, B. (2013). A Concise Introduction to Models and Methods for Automated Planning. Morgan & Claypool Publishers.——聚焦FOND问题
Ghallab, M., Nau, D., & Traverso, P. (2016). Automated planning and acting. Cambridge University Press.

QNP¶

Srivastava, S., Zilberstein, S., Immerman, N., & Geffner, H. (2011). Qualitative numeric planning. In AAAI.——详细阐述了FOND问题的解对应着QNP问题的解（并非互推关系），并介绍了SCC算法等内容
Srivastava, S., Immerman, N., & Zilberstein, S. (2011). A new representation and associated algorithms for generalized planning. Artificial Intelligence, 175(2), 615-647.——提出了利用QNP有效表述GP（通用规划）的方法

结合逻辑神经机¶

Garnelo, M., & Shanahan, M. (2019). Reconciling deep learning with symbolic artificial intelligence: representing objects and relations. Current Opinion in Behavioral Sciences, 29, 17-23.（将深度学习与符号人工智能相结合：表示对象与关系）
Toyer, S., Trevizan, F., Thiebaux, S., & Xie, L. (2018). Action schema networks: Generalised policies with deep learning. In AAAI.——神经网络生成通用策略
Groshev, E., Goldstein, M., Tamar, A., Srivastava, S., & Abbeel, P. (2018). Learning generalized reactive policies using deep neural networks. In Proc. ICAPS, Vol. 2018, pp. 408-416.——神经网络生成策略
Fern, A., Yoon, S., & Givan, R. (2004). Approximate policy iteration with a policy language bias. In Advances in neural information processing systems, pp. 847-854.
Boutilier, C., Reiter, R., & Price, B. (2001). Symbolic dynamic programming for first-order MDPs. In Proc. IJCAI, Vol. 1, pp. 690-700.（一阶马尔可夫过程的动态规划，**MDP**的马尔可夫数学化研究过程能够自然地与**RL**强化学习相衔接）
Van Otterlo, M. (2012). Solving relational and first-order logical markov decision processes: A survey. In Wiering, M., & van Otterlo, M. (Eds.), Reinforcement Learning, pp. 253-292. Springer.
Sukhbaatar, S., Szlam, A., Synnaeve, G., Chintala, S., & Fergus, R. (2015). Mazebase: A sandbox for learning from games. arXiv preprint arXiv:1511.07401.
Wang, C., Joshi, S., & Khardon, R. (2008). First order decision diagrams for relational MDPs. Journal of Artificial Intelligence Research, 31, 431-472.（一阶决策图对应求解MDP）
Sanner, S., & Boutilier, C. (2009). Practical solution techniques for first-order MDPs. Artificial Intelligence, 173(5-6), 748-788.
Nebel, B. (2000). On the compilability and expressive power of propositional planning. Journal of Artificial Intelligence Research, 12, 271-315.
Khardon, R. (1999). Learning action strategies for planning domains. Artificial Intelligence, 113, 125-148.
Issakkimuthu, M., Fern, A., & Tadepalli, P. (2018). Training deep reactive policies for probabilistic planning problems. In ICAPS.——概率规划问题

SAT¶

Een, N., & Sorensson, N. (2004). An extensible SAT-solver. Lecture notes in computer science, 2919, 502-518.

计算复杂性¶

Rintanen, J. (2004). Complexity of planning with partial observability. In Proc. ICAPS, pp. 345-354.
Levesque, H. J. (2005). Planning with loops. In IJCAI, pp. 509-515.——指数级复杂度
Littman, M. L., Goldsmith, J., & Mundhenk, M. (1998). The computational complexity of probabilistic planning. Journal of Artificial Intelligence Research, 9, 1-36.——表明QNP问题具有指数级别的计算复杂度

其他相关文献¶

Russell, S., & Norvig, P. (2002). Artificial Intelligence: A Modern Approach. Prentice Hall. 2^nd Edition.（人工智能教材）
Sipser, M. (2006). Introduction to Theory of Computation (2^nd edition). Thomson Course Technology, Boston, MA.（计算理论教材）
Cimatti, A., Roveri, M., & Traverso, P. (1998). Automatic OBDD-based generation of universal plans in non-deterministic domains. In Proc. AAAI-98, pp. 875-881.（OBDD——一种广为采用的紧凑表达结构）
Bajpai, A. N., Garg, S., et al. (2018). Transfer of deep reactive policies for MDP planning. In Advances in Neural Information Processing Systems, pp. 10965-10975.——面向无限随机域的通用规划
Helmert, M. (2002). Decidability and undecidability results for planning with numerical state variables. In Proc. AIPS, pp. 44-53.
Hu, Y., & De Giacomo, G. (2013). A generic technique for synthesizing bounded finite-state controllers. In Proc. ICAPS.
Srivastava, S., Zilberstein, S., Gupta, A., Abbeel, P., & Russell, S. (2015). Tractability of planning with loops. In Proc. AAAI.
Tarjan, R. (1972). Depth-first search and linear graph algorithms. SIAM journal on computing, 1(2), 146-160.
Pnueli, A. (1977). The temporal logic of programs. In 18^th Annual Symposium on Foundations of Computer Science, pp. 46-57. IEEE.
Pnueli, A., & Rosner, R. (1989). On the synthesis of an asynchronous reactive module. In ICALP, pp. 652-671.