Fetching the paper…
Reading the bibliography…
This paper considers the problem of learning temporal task specifications, e.g.
Jaynes, E.T.: Information theory and statistical mechanics. Physical review 106
1957
Earlier work this paper cites.
Luce, R.D.: Individual choice behavior. (1959)
1959
Earlier work this paper cites.
Kalman, R.E.: When is a linear control system optimal (1964)
1964
Earlier work this paper cites.
Skiscim, C.C., Golden, B.L.: Optimization by simulated annealing: A preliminary computational study for the TSP. In: WSC. pp. 523–535. ACM (1983)
1983
Earlier work this paper cites.
Ng, A.Y., Russell, S.J.: Algorithms for inverse reinforcement learning. In: ICML. pp. 663–670. Morgan Kaufmann (2000)
2000
Earlier work this paper cites.
Denis, F.: Learning regular languages from simple positive examples. Mach. Learn. 44
2001
Earlier work this paper cites.
Abbeel, P., Ng, A.Y.: Apprenticeship learning via inverse reinforcement learning. In: Proceedings of the twenty-first international conference on Machine learning. p. 1. ACM (2004)
2004
Earlier work this paper cites.
Ramachandran, D., Amir, E.: Bayesian inverse reinforcement learning. IJCAI (2007)
2007
Earlier work this paper cites.
Ziebart, B.D., Maas, A.L., Bagnell, J.A., Dey, A.K.: Maximum entropy inverse reinforcement learning. In: AAAI. vol. 8, pp. 1433–1438. Chicago, IL, USA (2008)
2008
Cited alongside, same era.
Heule, M., Verwer, S.: Exact DFA identification using SAT solvers. In: ICGI. Lecture Notes in Computer Science, vol. 6339, pp. 66–79. Springer (2010)
2010
Cited alongside, same era.
De la Higuera, C.: Grammatical inference: learning automata and grammars. Cambridge University Press (2010)
2010
Cited alongside, same era.
Ziebart, B.D., Bagnell, J.A., Dey, A.K.: Modeling interaction via the principle of maximum causal entropy (2010)
2010
Cited alongside, same era.
Cover, T.M., Thomas, J.A.: Elements of information theory. John Wiley & Sons (2012)
2012
Cited alongside, same era.
Shah, A., Kamath, P., Shah, J.A., Li, S.: Bayesian inference of temporal task specifications from demonstrations. In: NeurIPS. pp. 3808–3817 (2018)
2018
Later among the works it cites.
Vazquez-Chanlatte, M., Jha, S., Tiwari, A., Ho, M.K., Seshia, S.A.: Learning task specifications from demonstrations. In: NeurIPS. pp. 5372–5382 (2018)
2018
Later among the works it cites.
Chou, G., Ozay, N., Berenson, D.: Explaining multi-stage tasks by learning temporal logic formulas from suboptimal demonstrations. In: Robotics: Science and Systems (2020)
2020
Later among the works it cites.
Vazquez-Chanlatte, M., Seshia, S.A.: Maximum causal entropy specification inference from demonstrations. In: CAV (2). Lecture Notes in Computer Science, vol. 12225, pp. 255–278. Springer (2020)
2020
Later among the works it cites.
Abel, D., Dabney, W., Harutyunyan, A., Ho, M.K., Littman, M.L., Precup, D., Singh, S.: On the expressivity of markov reward. In: NeurIPS (2021)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ulyantsev, V., Zakirzyanov, I., Shalyto, A.: Bfs-based symmetry breaking predicates for DFA identification. In: LATA. Lecture Notes in Computer Science, vol. 8977, pp. 611–622. Springer (2015)
2015
Cited alongside, same era.
Kasenberg, D., Scheutz, M.: Interpretable apprenticeship learning with temporal logic specifications. In: CDC. pp. 4914–4921. IEEE (2017)
2017
Cited alongside, same era.
2021
Closest in time.
Carrillo, E.: Controller synthesis and Formal Behavior Inference in Autonomous Systems. Ph.D. thesis, University of Maryland, College Park, MD, USA (2021)
2021
Closest in time.
Yoon, H., Sankaranarayanan, S.: Predictive runtime monitoring for mobile robots using logic-based bayesian intent inference. In: ICRA. pp. 8565–8571. IEEE (2021)
2021
Closest in time.