A Scalable Scheme for Counting Linear Extensions

Talvitie, Topi; Kangas, Kustaa; Niinimäki, Teppo; Koivisto, Mikko

doi:10.24963/ijcai.2018/710

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…If no explicit mapping is required, the agent attempts to learn the abstraction of the MPD, which are invariant even though actions and state variables change [36], [37]. Another approach is to learn inter-task mapping automatically [38], [39] using novel mapping learning methods. Applying heuristics in transfer learning is proven to have reasonable acceleration in target task learning.…”

Section: Recent and Relevant Workmentioning

confidence: 99%

Mobile Service Robot Path Planning Using Deep Reinforcement Learning

Kumaar,

Kochuvila

2023

IEEE Access

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A mobile service robot operates in a constantly changing environment with other robots and humans. The service environment is usually vast and unknown, and the robot is expected to operate continuously for a long period. The environment can be dynamic, leading to the generation of new routes or the permanent blocking of old routes. The traditional path planner that relies on static maps will not suffice for a dynamic environment. This work is focused on developing a reinforcement learning-based path planner for a dynamic environment. The proposed system uses the deep Q-Learning algorithm to learn the initial paths using a topological map of the environment. In an environmental change, the proposed β β β-decay transfer learning algorithm trains the agent in the new environment. This algorithm uses experience vs. exploration vs. exploitation-based training depending on the similarity of the old and new environments. The system is implemented on the Robotic Operating System framework and tested using Turtlebot3 mobile robot in the Gazebo simulator. The experimental results show that the reinforcement learning system learns all the routes based on the initial topological map of different service environments with an accuracy of over 98%. A comparative analysis of the β β β-decay transfer learning and non-transfer learning agents is performed based on various evaluation metrics. The transfer learning agent converges twice faster than the non-transfer learning agent.

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Section: Recent and Relevant Workmentioning

confidence: 99%

Mobile Service Robot Path Planning Using Deep Reinforcement Learning

Kumaar,

Kochuvila

2023

IEEE Access

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“…Several fully polynomialtime randomized schemes have also been designed, which are based on Markov chain Monte Carlo (MCMC) schemes such as the telescopic product estimator and the Tootsie Pop algorithm. Recently, [43] present a novel scheme called relaxation Tootsie Pop, and showed that it is superior to all previous schemes (including the scheme of encoding the problem into #SAT).…”

Section: Counting Linear Extensionsmentioning

confidence: 99%

Counting the Number of Solutions to Constraints

Zhang¹,

Ge²,

Ma³

2020

Preprint

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Compared with constraint satisfaction problems, counting problems have received less attention. In this paper, we survey research works on the problems of counting the number of solutions to constraints. The constraints may take various forms, including, formulas in the propositional logic, linear inequalities over the reals or integers, Boolean combination of linear constraints. We describe some techniques and tools for solving the counting problems, as well as some applications (e.g., applications to automated reasoning, program analysis, formal verification and information security).

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“…From a practical viewpoint, however, (asymptotic) worst-case bounds are only of secondary interest: it would suffice that an algorithm outputs an estimate that, with high probability, is guaranteed to be within a small relative error of the exact value-no good upper bound for the running time is required a priori; it is typically satisfactory that the algorithm runs fast on the instances one encounters in practice. The artificial intelligence research community, in particular, has found this paradigm attractive for problems in various domains, including probabilistic inference [1,7], weighted model counting [12,11], network reliability [26], and counting linear extensions [35].…”

Section: Introductionmentioning

confidence: 99%

Approximating the Permanent with Deep Rejection Sampling

Harviainen,

Röyskö,

Koivisto

2021

Preprint

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We present a randomized approximation scheme for the permanent of a matrix with nonnegative entries. Our scheme extends a recursive rejection sampling method of Huber and Law (SODA 2008) by replacing the upper bound for the permanent with a linear combination of the subproblem bounds at a moderately large depth of the recursion tree. This method, we call deep rejection sampling, is empirically shown to outperform the basic, depth-zero variant, as well as a related method by Kuck et al. (NeurIPS 2019). We analyze the expected running time of the scheme on random (0, 1)-matrices where each entry is independently 1 with probability p. Our bound is superior to a previous one for p less than 1/5, matching another bound that was known to hold when every row and column has density exactly p.

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A Scalable Scheme for Counting Linear Extensions

Cited by 4 publications

References 7 publications

Mobile Service Robot Path Planning Using Deep Reinforcement Learning

Mobile Service Robot Path Planning Using Deep Reinforcement Learning

Counting the Number of Solutions to Constraints

Approximating the Permanent with Deep Rejection Sampling

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