Journal articles: 'Constrained exploration'

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Pankayaraj, Pathmanathan, and Pradeep Varakantham. "Constrained Reinforcement Learning in Hard Exploration Problems." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (June 26, 2023): 15055–63. http://dx.doi.org/10.1609/aaai.v37i12.26757.

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One approach to guaranteeing safety in Reinforcement Learning is through cost constraints that are dependent on the policy. Recent works in constrained RL have developed methods that ensure constraints are enforced even at learning time while maximizing the overall value of the policy. Unfortunately, as demonstrated in our experimental results, such approaches do not perform well on complex multi-level tasks, with longer episode lengths or sparse rewards. To that end, we propose a scalable hierarchical approach for constrained RL problems that employs backward cost value functions in the context of task hierarchy and a novel intrinsic reward function in lower levels of the hierarchy to enable cost constraint enforcement. One of our key contributions is in proving that backward value functions are theoretically viable even when there are multiple levels of decision making. We also show that our new approach, referred to as Hierarchically Limited consTraint Enforcement (HiLiTE) significantly improves on state of the art Constrained RL approaches for many benchmark problems from literature. We further demonstrate that this performance (on value and constraint enforcement) clearly outperforms existing best approaches for constrained RL and hierarchical RL.

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Mellado, Nicolas, David Vanderhaeghe, Charlotte Hoarau, Sidonie Christophe, Mathieu Brédif, and Loic Barthe. "Constrained palette-space exploration." ACM Transactions on Graphics 36, no. 4 (July 20, 2017): 1–14. http://dx.doi.org/10.1145/3072959.3073650.

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Duncan, Christian A., Stephen G. Kobourov, and V. S. Anil Kumar. "Optimal constrained graph exploration." ACM Transactions on Algorithms 2, no. 3 (July 2006): 380–402. http://dx.doi.org/10.1145/1159892.1159897.

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Yang, Qisong, and Matthijs T. J. Spaan. "CEM: Constrained Entropy Maximization for Task-Agnostic Safe Exploration." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (June 26, 2023): 10798–806. http://dx.doi.org/10.1609/aaai.v37i9.26281.

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In the absence of assigned tasks, a learning agent typically seeks to explore its environment efficiently. However, the pursuit of exploration will bring more safety risks. An under-explored aspect of reinforcement learning is how to achieve safe efficient exploration when the task is unknown. In this paper, we propose a practical Constrained Entropy Maximization (CEM) algorithm to solve task-agnostic safe exploration problems, which naturally require a finite horizon and undiscounted constraints on safety costs. The CEM algorithm aims to learn a policy that maximizes state entropy under the premise of safety. To avoid approximating the state density in complex domains, CEM leverages a k-nearest neighbor entropy estimator to evaluate the efficiency of exploration. In terms of safety, CEM minimizes the safety costs, and adaptively trades off safety and exploration based on the current constraint satisfaction. The empirical analysis shows that CEM enables the acquisition of a safe exploration policy in complex environments, resulting in improved performance in both safety and sample efficiency for target tasks.

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Ivanov, Alexander, and Mark Campbell. "Uncertainty Constrained Robotic Exploration: An Integrated Exploration Planner." IEEE Transactions on Control Systems Technology 27, no. 1 (January 2019): 146–60. http://dx.doi.org/10.1109/tcst.2017.2759729.

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Ahuir, J., A. S. Brun, and A. Strugarek. "From stellar coronae to gyrochronology: A theoretical and observational exploration." Astronomy & Astrophysics 635 (March 2020): A170. http://dx.doi.org/10.1051/0004-6361/201936974.

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Context. Stellar spin down is the result of a complex process involving rotation, dynamo, wind, and magnetism. Multiwavelength surveys of solar-like stars have revealed the likely existence of relationships between their rotation, X-ray luminosity, mass losses, and magnetism. They impose strong constraints on the corona and wind of cool stars. Aims. We aim to provide power-law prescriptions of the mass loss of stars, of their magnetic field, and of their base coronal density and temperature that are compatible with their observationally-constrained spin down. Methods. We link the magnetic field and the mass-loss rate from a wind torque formulation, which is in agreement with the distribution of stellar rotation periods in open clusters and the Skumanich law. Given a wind model and an expression of the X-ray luminosity from radiative losses, we constrained the coronal properties by assuming different physical scenarios linking closed loops to coronal holes. Results. We find that the magnetic field and the mass loss are involved in a one-to-one correspondence that is constrained from spin down considerations. We show that a magnetic field, depending on both the Rossby number and the stellar mass, is required to keep a consistent spin down model. The estimates of the magnetic field and the mass-loss rate obtained from our formalism are consistent with statistical studies as well as individual observations and they give new leads to constrain the magnetic field-rotation relation. The set of scaling-laws we derived can be broadly applied to cool stars from the pre-main sequence to the end of the main sequence (MS), and they allow for stellar wind modeling that is consistent with all of the observational constraints available to date.

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Deng, Bailin, Sofien Bouaziz, Mario Deuss, Alexandre Kaspar, Yuliy Schwartzburg, and Mark Pauly. "Interactive design exploration for constrained meshes." Computer-Aided Design 61 (April 2015): 13–23. http://dx.doi.org/10.1016/j.cad.2014.01.004.

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Yang, Yong-Liang, Yi-Jun Yang, Helmut Pottmann, and Niloy J. Mitra. "Shape space exploration of constrained meshes." ACM Transactions on Graphics 30, no. 6 (December 2011): 1–12. http://dx.doi.org/10.1145/2070781.2024158.

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Angmalisang, Helen Yuliana, Syaiful Anam, and Sobri Abusini. "Leaders and followers algorithm for constrained non-linear optimization." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 1 (January 1, 2019): 162. http://dx.doi.org/10.11591/ijeecs.v13.i1.pp162-169.

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<p>Leaders and Followers algorithm was a novel metaheuristics proposed by Yasser Gonzalez-Fernandez and Stephen Chen. In solving unconstrained optimization, it performed better exploration than other well-known metaheuristics, e.g. Genetic Algorithm, Particle Swarm Optimization and Differential Evolution. Therefore, it performed well in multi-modal problems. In this paper, Leaders and Followers was modified for constrained non-linear optimization. Several well-known benchmark problems for constrained optimization were used to evaluate the proposed algorithm. The result of the evaluation showed that the proposed algorithm consistently and successfully found the optimal solution of low dimensional constrained optimization problems and high dimensional optimization with high number of linear inequality constraint only. Moreover, the proposed algorithm had difficulty in solving high dimensional optimization problem with non-linear constraints and any problem which has more than one equality constraint. In the comparison with other metaheuristics, Leaders and Followers had better performance in overall benchmark problems.</p>

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Francis, Gilad, Lionel Ott, Roman Marchant, and Fabio Ramos. "Occupancy map building through Bayesian exploration." International Journal of Robotics Research 38, no. 7 (May 6, 2019): 769–92. http://dx.doi.org/10.1177/0278364919846549.

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We propose a novel holistic approach to safe autonomous exploration and map building based on constrained Bayesian optimization. This method finds optimal continuous paths instead of discrete sensing locations that inherently satisfy motion and safety constraints. Evaluating both the objective and constraints functions requires forward simulation of expected observations. As such, evaluations are costly, and therefore the Bayesian optimizer proposes only paths that are likely to yield optimal results and satisfy the constraints with high confidence. By balancing the reward and risk associated with each path, the optimizer minimizes the number of expensive function evaluations. We demonstrate the effectiveness of our approach in a series of experiments both in simulation and with a real ground robot and provide comparisons with other exploration techniques. The experimental results show that our method provides robust and consistent performance in all tests and performs better than or as good as the state of the art.

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Gupta, Surabhi, and Sudhir Kumar Gupta. "An information theoretic exploration of constrained MSSM." Nuclear Physics B 965 (April 2021): 115336. http://dx.doi.org/10.1016/j.nuclphysb.2021.115336.

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Dwianto, Yohanes Bimo, and Ardanto Mohammad Pramutadi. "Drag Minimization of Low Subsonic Airfoil with Constrained Genetic Algorithm." Mesin 29, no. 2 (December 28, 2023): 27–40. http://dx.doi.org/10.5614/mesin.2023.29.2.3.

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Drag minimization of low subsonic airfoil was conducted with constrained genetic algorithm (CGA). To cope with the constraints, each of these two different types of constraint handling techniques (CHTs), namely Superiority of Feasible Individual (SoF) and Generalized Multiple Constraint Ranking (G-MCR) were employed to the CGA and compared. From three independent runs for each CHT, it was obtained that G-MCR performed significantly better than SoF, indicating that G-MCR, a novel type of CHT, provides better exploration of the design space to obtain better solution. The obtained best airfoil designs were compared with a baseline airfoil and analyzed. The best optimum airfoil increases the aerodynamic efficiency by 21.4%. It was observed that the reduction of drag only occurs locally, so that a robust optimization is required in the future.

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Vallée, Marc A., William A. Morris, Stéphane Perrouty, Robert G. Lee, Ken Wasyliuk, Julia J. King, Kevin Ansdell, et al. "Geophysical inversion contributions to mineral exploration: lessons from the Footprints project." Canadian Journal of Earth Sciences 56, no. 5 (May 2019): 525–43. http://dx.doi.org/10.1139/cjes-2019-0009.

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Magnetic and gravity inversions are used to create 2D or 3D models of the magnetic susceptibility and density, respectively, using potential field data. Unconstrained inversions generate an output based on mathematical constraints imposed by the inversion algorithm. Constrained inversions integrate lithological, structural, and petrophysical information in the inversion process to produce more geologically meaningful results. This study analyses the validity of this assertion in the context of the NSERC-CMIC Mineral Exploration Footprints project. Unconstrained and constrained geophysical inversions were computed for three mining sites: a gold site (Canadian Malartic, Québec), a copper site (Highland Valley, British Columbia), and a uranium site (Millennium – McArthur River, Saskatchewan). After initially computing unconstrained inversions, constrained inversions were developed using physical property measurements, which directly link geophysics to geology, and lithological boundaries extracted from an interpreted geological model. While each derived geological model is consistent with the geophysical data, each site exhibited some magnetic complexity that confounded the inversion. The gold site includes regions with a strong magnetic signature that masks the more weakly magnetic zone, thereby hiding the magnetic signature associated with the ore body. Initial unconstrained inversions for the copper site yielded solutions with invalid depth extent. A consistency between the constrained model and the geological model is reached with iterative changes to the depth extent of the model. At the uranium site, the observed magnetic signal is weak, but the inversion provided some insights that could be interpreted in terms of an already known complexly folded geological model.

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Sun, Ying, and Yuelin Gao. "An improved composite particle swarm optimization algorithm for solving constrained optimization problems and its engineering applications." AIMS Mathematics 9, no. 4 (2024): 7917–44. http://dx.doi.org/10.3934/math.2024385.

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<abstract><p>In the last few decades, the particle swarm optimization (PSO) algorithm has been demonstrated to be an effective approach for solving real-world optimization problems. To improve the effectiveness of the PSO algorithm in finding the global best solution for constrained optimization problems, we proposed an improved composite particle swarm optimization algorithm (ICPSO). Based on the optimization principles of the PSO algorithm, in the ICPSO algorithm, we constructed an evolutionary update mechanism for the personal best position population. This mechanism incorporated composite concepts, specifically the integration of the $ \varepsilon $-constraint, differential evolution (DE) strategy, and feasibility rule. This approach could effectively balance the objective function and constraints, and could improve the ability of local exploitation and global exploration. Experiments on the CEC2006 and CEC2017 benchmark functions and real-world constraint optimization problems from the CEC2020 dataset showed that the ICPSO algorithm could effectively solve complex constrained optimization problems.</p></abstract>

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Bacanin, Nebojsa, and Milan Tuba. "Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Optimization Problem with Entropy Diversity Constraint." Scientific World Journal 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/721521.

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Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.

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Yang, Jian Chun, and Wen Long. "Improved Grey Wolf Optimization Algorithm for Constrained Mechanical Design Problems." Applied Mechanics and Materials 851 (August 2016): 553–58. http://dx.doi.org/10.4028/www.scientific.net/amm.851.553.

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An improved grey wolf optimization (IGWO) algorithm is proposed for solving constrained mechanical design problems in this paper. In proposed IGWO algorithm, a novel nonlinearly update equation of convergence factor based on sines function is presented to balance the exploration ability and exploitation ability. The feasibility-based rules based on tournament selection was introduced to handle constrains. Simulation results and comparisons with other state-of-the-art algorithms using three constrained mechanical design problems are provided.

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Jamgochian, Arec, Anthony Corso, and Mykel J. Kochenderfer. "Online Planning for Constrained POMDPs with Continuous Spaces through Dual Ascent." Proceedings of the International Conference on Automated Planning and Scheduling 33, no. 1 (July 1, 2023): 198–202. http://dx.doi.org/10.1609/icaps.v33i1.27195.

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Rather than augmenting rewards with penalties for undesired behavior, Constrained Partially Observable Markov Decision Processes (CPOMDPs) plan safely by imposing inviolable hard constraint value budgets. Previous work performing online planning for CPOMDPs has only been applied to discrete action and observation spaces. In this work, we propose algorithms for online CPOMDP planning for continuous state, action, and observation spaces by combining dual ascent with progressive widening. We empirically compare the effectiveness of our proposed algorithms on continuous CPOMDPs that model both toy and real-world safety-critical problems. Additionally, we compare against the use of online solvers for continuous unconstrained POMDPs that scalarize cost constraints into rewards and highlight the limitations of the default exploration scheme.

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Zhou, Ding, Zhenhua Yu, Yanquan Zhang, and Shunli Li. "Translational and rotational motion planning for spacecraft close proximity using sampling-based methods." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 10 (October 9, 2018): 3680–99. http://dx.doi.org/10.1177/0954410018803986.

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For autonomous spacecraft close proximity under environments containing multiple obstacles and complicated constraints, incrementally rapid planning approaches stemming from sampling-based methods are investigated in this paper. Exploring planners are separately developed for the impulsive maneuvered translation and the piecewise constant controlled rotation, which, however, is constrained by the pointing limits coupling with relative positions during the proximity. Using a cost-informed parent-connecting strategy originating from dynamic programming as well as a sweeping growth fashion balanced between tree-based and graph-based methods, an asymptotically optimal unidirectional exploration method is proposed to search energy-efficient translational trajectory without collision. As for the rotation planning, the pointing constraints are taken as virtual obstacles in the state-space augmented with time horizon planned by the translation and, accordingly, a bidirectional exploration method is developed to generate constraint-satisfied slew paths with fast convergence rate. Numerical experiments indicate that the proposed sampling-based methods can rapidly return asymptotic optimal translation trajectory and rotation path satisfying collision avoidance and sensor field-of-view constraints.

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Bairamkulov, Rassul, Kan Xu, Mikhail Popovich, Juan S. Ochoa, Vaishnav Srinivas, and Eby G. Friedman. "Power Delivery Exploration Methodology Based on Constrained Optimization." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 39, no. 9 (September 2020): 1916–24. http://dx.doi.org/10.1109/tcad.2019.2925397.

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Yang, Qisong, Thiago D. Simão, Simon H. Tindemans, and Matthijs T. J. Spaan. "WCSAC: Worst-Case Soft Actor Critic for Safety-Constrained Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 10639–46. http://dx.doi.org/10.1609/aaai.v35i12.17272.

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Safe exploration is regarded as a key priority area for reinforcement learning research. With separate reward and safety signals, it is natural to cast it as constrained reinforcement learning, where expected long-term costs of policies are constrained. However, it can be hazardous to set constraints on the expected safety signal without considering the tail of the distribution. For instance, in safety-critical domains, worst-case analysis is required to avoid disastrous results. We present a novel reinforcement learning algorithm called Worst-Case Soft Actor Critic, which extends the Soft Actor Critic algorithm with a safety critic to achieve risk control. More specifically, a certain level of conditional Value-at-Risk from the distribution is regarded as a safety measure to judge the constraint satisfaction, which guides the change of adaptive safety weights to achieve a trade-off between reward and safety. As a result, we can optimize policies under the premise that their worst-case performance satisfies the constraints. The empirical analysis shows that our algorithm attains better risk control compared to expectation-based methods.

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Yu, Baosheng, Meng Fang, and Dacheng Tao. "Per-Round Knapsack-Constrained Linear Submodular Bandits." Neural Computation 28, no. 12 (December 2016): 2757–89. http://dx.doi.org/10.1162/neco_a_00887.

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Linear submodular bandits has been proven to be effective in solving the diversification and feature-based exploration problem in information retrieval systems. Considering there is inevitably a budget constraint in many web-based applications, such as news article recommendations and online advertising, we study the problem of diversification under a budget constraint in a bandit setting. We first introduce a budget constraint to each exploration step of linear submodular bandits as a new problem, which we call per-round knapsack-constrained linear submodular bandits. We then define an [Formula: see text]-approximation unit-cost regret considering that the submodular function maximization is NP-hard. To solve this new problem, we propose two greedy algorithms based on a modified UCB rule. We prove these two algorithms with different regret bounds and computational complexities. Inspired by the lazy evaluation process in submodular function maximization, we also prove that a modified lazy evaluation process can be used to accelerate our algorithms without losing their theoretical guarantee. We conduct a number of experiments, and the experimental results confirm our theoretical analyses.

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Ding, Yuhao, and Javad Lavaei. "Provably Efficient Primal-Dual Reinforcement Learning for CMDPs with Non-stationary Objectives and Constraints." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (June 26, 2023): 7396–404. http://dx.doi.org/10.1609/aaai.v37i6.25900.

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We consider primal-dual-based reinforcement learning (RL) in episodic constrained Markov decision processes (CMDPs) with non-stationary objectives and constraints, which plays a central role in ensuring the safety of RL in time-varying environments. In this problem, the reward/utility functions and the state transition functions are both allowed to vary arbitrarily over time as long as their cumulative variations do not exceed certain known variation budgets. Designing safe RL algorithms in time-varying environments is particularly challenging because of the need to integrate the constraint violation reduction, safe exploration, and adaptation to the non-stationarity. To this end, we identify two alternative conditions on the time-varying constraints under which we can guarantee the safety in the long run. We also propose the Periodically Restarted Optimistic Primal-Dual Proximal Policy Optimization (PROPD-PPO) algorithm that can coordinate with both two conditions. Furthermore, a dynamic regret bound and a constraint violation bound are established for the proposed algorithm in both the linear kernel CMDP function approximation setting and the tabular CMDP setting under two alternative conditions. This paper provides the first provably efficient algorithm for non-stationary CMDPs with safe exploration.

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Garcelon, Evrard, Mohammad Ghavamzadeh, Alessandro Lazaric, and Matteo Pirotta. "Improved Algorithms for Conservative Exploration in Bandits." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3962–69. http://dx.doi.org/10.1609/aaai.v34i04.5812.

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In many fields such as digital marketing, healthcare, finance, and robotics, it is common to have a well-tested and reliable baseline policy running in production (e.g., a recommender system). Nonetheless, the baseline policy is often suboptimal. In this case, it is desirable to deploy online learning algorithms (e.g., a multi-armed bandit algorithm) that interact with the system to learn a better/optimal policy under the constraint that during the learning process the performance is almost never worse than the performance of the baseline itself. In this paper, we study the conservative learning problem in the contextual linear bandit setting and introduce a novel algorithm, the Conservative Constrained LinUCB (CLUCB2). We derive regret bounds for CLUCB2 that match existing results and empirically show that it outperforms state-of-the-art conservative bandit algorithms in a number of synthetic and real-world problems. Finally, we consider a more realistic constraint where the performance is verified only at predefined checkpoints (instead of at every step) and show how this relaxed constraint favorably impacts the regret and empirical performance of CLUCB2.

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Song, Jianping, Hong Ni, and Xiaoyong Zhu. "A Distributed Multicast QoS Routing Construction Approach in Information-Centric Networking." Applied Sciences 13, no. 24 (December 18, 2023): 13349. http://dx.doi.org/10.3390/app132413349.

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Many applications suitable for multicast transmission, such as video conferencing and live e-commerce, demand high Quality of Service (QoS) and require data delivery to be completed within specified delay constraints. Some methods have been proposed for constructing delay-constrained multicast routing based on network state. However, obtaining precise network latency can be challenging, resulting in inaccuracies in delay-constrained routing calculations and, ultimately, the inability to meet application requirements. Additionally, many methods engage in an indiscriminate exploration of potential paths in the network, causing significant message processing overhead. This paper proposes an Information-Centric Networking (ICN)-based approach for delay-constrained multicast routing. Our method dynamically constructs multicast paths from tree nodes to receivers based on real-time path status detection during the join message propagation phase. Additionally, we present a method for acquiring neighborhood state information to facilitate real-time routing decisions. To curtail indiscriminate path exploration, our approach uses the ICN Name Resolution System (NRS) to obtain and select potential optimal tree nodes. For this purpose, we design a multicast service registration and resolution mechanism using the ICN Name Resolution System (NRS). Simulation results indicate that our approach exhibits a higher success ratio and concurrently incurs lower message processing overhead than some other methods, particularly in situations with stringent delay constraints.

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Brütting, Jan, Patrick Ole Ohlbrock, Julian Hofer, and Pierluigi D’Acunto. "Stock-constrained truss design exploration through combinatorial equilibrium modeling." International Journal of Space Structures 36, no. 4 (December 2021): 253–69. http://dx.doi.org/10.1177/09560599211064100.

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Reusing structural components has potential to reduce environmental impacts of building structures because it reduces new material use, energy consumption, and waste. When designing structures through reuse, available element characteristics become a design input. This paper presents a new computational workflow to design structures made of reused and new elements. The workflow combines Combinatorial Equilibrium Modeling, efficient Best-Fit heuristics, and Life Cycle Assessment to explore different design options in a user-interactive way and with almost real-time feedback. The method applicability is demonstrated by a realistic case study. Results show that structures combining reused and new elements have a significantly lower environmental impact than solutions made of new material only.

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Chung, Jen Jen, Nicholas R. J. Lawrance, and Salah Sukkarieh. "Learning to soar: Resource-constrained exploration in reinforcement learning." International Journal of Robotics Research 34, no. 2 (December 16, 2014): 158–72. http://dx.doi.org/10.1177/0278364914553683.

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Sasena, Michael J., Panos Papalambros, and Pierre Goovaerts. "Exploration of Metamodeling Sampling Criteria for Constrained Global Optimization." Engineering Optimization 34, no. 3 (January 2002): 263–78. http://dx.doi.org/10.1080/03052150211751.

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Das, Shantanu, Dariusz Dereniowski, and Christina Karousatou. "Collaborative Exploration of Trees by Energy-Constrained Mobile Robots." Theory of Computing Systems 62, no. 5 (October 30, 2017): 1223–40. http://dx.doi.org/10.1007/s00224-017-9816-3.

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Chen, Runfeng, Jie Li, and Ting Peng. "Decentralized UAV Swarm Scheduling with Constrained Task Exploration Balance." Drones 7, no. 4 (April 13, 2023): 267. http://dx.doi.org/10.3390/drones7040267.

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Scheduling is one of the key technologies used in unmanned aerial vehicle (UAV) swarms. Scheduling determines whether a task can be completed and when the task is complete. The distributed method is a fast way to realize swarm scheduling. It has no central node and UAVs can freely join or leave it, thus making it more robust and flexible. However, the two most representative methods, the Consensus-Based Bundle Algorithm (CBBA) and the Performance Impact (PI) algorithm, pursue the minimum cost impact of tasks, which have optimization limitations and are easily cause task conflicts. In this paper, a new concept called “task consideration” is proposed to quantify the impact of tasks on scheduling and the regression of the task itself, balancing the exploration of the UAV for the minimum-impact task and the regression of neighboring tasks to improve the optimization and convergence of scheduling. In addition, the conflict resolution rules are modified to fit the proposed method, and the exploration of tasks is increased by a new removal method to further improve the optimization. Finally, through extensive Monte Carlo experiments, compared with CBBA and PI, the proposed method is shown to perform better in terms of task allocation and total travel time, and with the increase in the number of average UAV tasks, the number of iterations is less and the convergence is faster.

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Rogers, Andrew, Kasra Eshaghi, Goldie Nejat, and Beno Benhabib. "Occupancy Grid Mapping via Resource-Constrained Robotic Swarms: A Collaborative Exploration Strategy." Robotics 12, no. 3 (May 9, 2023): 70. http://dx.doi.org/10.3390/robotics12030070.

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This paper addresses the problem of building an occupancy grid map of an unknown environment using a swarm comprising resource-constrained robots, i.e., robots with limited exteroceptive and inter-robot sensing capabilities. Past approaches have, commonly, used random-motion models to disperse the swarm and explore the environment randomly, which do not necessarily consider prior information already contained in the map. Herein, we present a collaborative, effective exploration strategy that directs the swarm toward ‘promising’ frontiers by dividing the swarm into two teams: landmark robots and mapper robots, respectively. The former direct the latter, toward promising frontiers, to collect proximity measurements to be incorporated into the map. The positions of the landmark robots are optimized to maximize new information added to the map while also adhering to connectivity constraints. The proposed strategy is novel as it decouples the problem of directing the resource-constrained swarm from the problem of mapping to build an occupancy grid map. The performance of the proposed strategy was validated through extensive simulated experiments.

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Gao, Weishang, Cheng Shao, and Yi An. "Bidirectional Dynamic Diversity Evolutionary Algorithm for Constrained Optimization." Mathematical Problems in Engineering 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/762372.

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Evolutionary algorithms (EAs) were shown to be effective for complex constrained optimization problems. However, inflexible exploration-exploitation and improper penalty in EAs with penalty function would lead to losing the global optimum nearby or on the constrained boundary. To determine an appropriate penalty coefficient is also difficult in most studies. In this paper, we propose a bidirectional dynamic diversity evolutionary algorithm (Bi-DDEA) with multiagents guiding exploration-exploitation through local extrema to the global optimum in suitable steps. In Bi-DDEA potential advantage is detected by three kinds of agents. The scale and the density of agents will change dynamically according to the emerging of potential optimal area, which play an important role of flexible exploration-exploitation. Meanwhile, a novel double optimum estimation strategy with objective fitness and penalty fitness is suggested to compute, respectively, the dominance trend of agents in feasible region and forbidden region. This bidirectional evolving with multiagents can not only effectively avoid the problem of determining penalty coefficient but also quickly converge to the global optimum nearby or on the constrained boundary. By examining the rapidity and veracity of Bi-DDEA across benchmark functions, the proposed method is shown to be effective.

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Benavides, Facundo, Caroline Ponzoni Carvalho Chanel, Pablo Monzón, and Eduardo Grampín. "An Auto-Adaptive Multi-Objective Strategy for Multi-Robot Exploration of Constrained-Communication Environments." Applied Sciences 9, no. 3 (February 9, 2019): 573. http://dx.doi.org/10.3390/app9030573.

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The exploration problem is a fundamental subject in autonomous mobile robotics that deals with achieving the complete coverage of a previously unknown environment. There are several scenarios where completing exploration of a zone is a main part of the mission. Due to the efficiency and robustness brought by multi-robot systems, exploration is usually done cooperatively. Wireless communication plays an important role in collaborative multi-robot strategies. Unfortunately, the assumption of stable communication and end-to-end connectivity may be easily compromised in real scenarios. In this paper, a novel auto-adaptive multi-objective strategy is followed to support the selection of tasks regarding both exploration performance and connectivity level. Compared with others, the proposed approach shows effectiveness and flexibility to tackle the multi-robot exploration problem, being capable of decreasing the last of disconnection periods without noticeable degradation of the completion exploration time.

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Bhattacharjee, Protim, Martin Burger, Anko Borner, and Veniamin I. Morgenshtern. "Region-of-Interest Prioritised Sampling for Constrained Autonomous Exploration Systems." IEEE Transactions on Computational Imaging 8 (2022): 302–16. http://dx.doi.org/10.1109/tci.2022.3163552.

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Ming, Yanbo, Guoqing Ma, Taihan Wang, Bingzhen Ma, Qingfa Meng, and Zongrui Li. "Power-Type Structural Self-Constrained Inversion Methods of Gravity and Magnetic Data." Remote Sensing 16, no. 4 (February 14, 2024): 681. http://dx.doi.org/10.3390/rs16040681.

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The inversion of gravity and magnetic data can obtain the density and magnetic structure of underground space, which provide important information for resource exploration and geological structure division. The most commonly used inversion method is smooth inversion in which the objective function is built with L2-norm, which has good stability, but it produces non-focused results that make subsequent interpretation difficult. The power-type structural self-constrained inversion (PTSS) method with L2-norm is proposed to improve the resolution of smooth inversion. A self-constraint term based on the power gradient of the results is introduced, which takes advantage of the structural feature that the power gradient can better focus on the model boundary to improve the resolution. For the joint inversion of gravity and magnetic data, the power-type mutual-constrained term between different physical structures and the self-constrained term can be simultaneously used to obtain higher-resolution results. The modeling tests demonstrated that the PTSS method can produce converged high-resolution results with good noise immunity in both the respective inversions and the joint inversion. Then, the PTSS joint inversion was applied to the airborne gravity and magnetic data of the iron ore district in Shandong, revealing the shape and location of the mineralized rock mass, which are crucial information for subsequent detailed exploration.

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Wang, Shengbo, and Ke Li. "Constrained Bayesian Optimization under Partial Observations: Balanced Improvements and Provable Convergence." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 14 (March 24, 2024): 15607–15. http://dx.doi.org/10.1609/aaai.v38i14.29488.

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The partially observable constrained optimization problems (POCOPs) impede data-driven optimization techniques since an infeasible solution of POCOPs can provide little information about the objective as well as the constraints. We endeavor to design an efficient and provable method for expensive POCOPs under the framework of constrained Bayesian optimization. Our method consists of two key components. Firstly, we present an improved design of the acquisition functions that introduce balanced exploration during optimization. We rigorously study the convergence properties of this design to demonstrate its effectiveness. Secondly, we propose Gaussian processes embedding different likelihoods as the surrogate model for partially observable constraints. This model leads to a more accurate representation of the feasible regions compared to traditional classification-based models. Our proposed method is empirically studied on both synthetic and real-world problems. The results demonstrate the competitiveness of our method for solving POCOPs.

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Ding, Zhenhuan, Xiaoge Huang, and Zhao Liu. "Active Exploration by Chance-Constrained Optimization for Voltage Regulation with Reinforcement Learning." Energies 15, no. 2 (January 16, 2022): 614. http://dx.doi.org/10.3390/en15020614.

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Voltage regulation in distribution networks encounters a challenge of handling uncertainties caused by the high penetration of photovoltaics (PV). This research proposes an active exploration (AE) method based on reinforcement learning (RL) to respond to the uncertainties by regulating the voltage of a distribution network with battery energy storage systems (BESS). The proposed method integrates engineering knowledge to accelerate the training process of RL. The engineering knowledge is the chance-constrained optimization. We formulate the problem in a chance-constrained optimization with a linear load flow approximation. The optimization results are used to guide the action selection of the exploration for improving training efficiency and reducing the conserveness characteristic. The comparison of methods focuses on how BESSs are used, training efficiency, and robustness under varying uncertainties and BESS sizes. We implement the proposed algorithm, a chance-constrained optimization, and a traditional Q-learning in the IEEE 13 Node Test Feeder. Our evaluation shows that the proposed AE method has a better response to the training efficiency compared to traditional Q-learning. Meanwhile, the proposed method has advantages in BESS usage in conserveness compared to the chance-constrained optimization.

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Yang, Yufei, and Changsheng Zhang. "A Multi-Objective Carnivorous Plant Algorithm for Solving Constrained Multi-Objective Optimization Problems." Biomimetics 8, no. 2 (March 26, 2023): 136. http://dx.doi.org/10.3390/biomimetics8020136.

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Satisfying various constraints and multiple objectives simultaneously is a significant challenge in solving constrained multi-objective optimization problems. To address this issue, a new approach is proposed in this paper that combines multi-population and multi-stage methods with a Carnivorous Plant Algorithm. The algorithm employs the ϵ-constraint handling method, with the ϵ value adjusted according to different stages to meet the algorithm’s requirements. To improve the search efficiency, a cross-pollination is designed based on the trapping mechanism and pollination behavior of carnivorous plants, thus balancing the exploration and exploitation abilities and accelerating the convergence speed. Moreover, a quasi-reflection learning mechanism is introduced for the growth process of carnivorous plants, enhancing the optimization efficiency and improving its global convergence ability. Furthermore, the quadratic interpolation method is introduced for the reproduction process of carnivorous plants, which enables the algorithm to escape from local optima and enhances the optimization precision and convergence speed. The proposed algorithm’s performance is evaluated on several test suites, including DC-DTLZ, FCP, DASCMOP, ZDT, DTLZ, and RWMOPs. The experimental results indicate competitive performance of the proposed algorithm over the state-of-the-art constrained multi-objective optimization algorithms.

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Balakrishnan, Avinash, Djallel Bouneffouf, Nicholas Mattei, and Francesca Rossi. "Incorporating Behavioral Constraints in Online AI Systems." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3–11. http://dx.doi.org/10.1609/aaai.v33i01.33013.

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AI systems that learn through reward feedback about the actions they take are increasingly deployed in domains that have significant impact on our daily life. However, in many cases the online rewards should not be the only guiding criteria, as there are additional constraints and/or priorities imposed by regulations, values, preferences, or ethical principles. We detail a novel online agent that learns a set of behavioral constraints by observation and uses these learned constraints as a guide when making decisions in an online setting while still being reactive to reward feedback. To define this agent, we propose to adopt a novel extension to the classical contextual multi-armed bandit setting and we provide a new algorithm called Behavior Constrained Thompson Sampling (BCTS) that allows for online learning while obeying exogenous constraints. Our agent learns a constrained policy that implements the observed behavioral constraints demonstrated by a teacher agent, and then uses this constrained policy to guide the reward-based online exploration and exploitation. We characterize the upper bound on the expected regret of the contextual bandit algorithm that underlies our agent and provide a case study with real world data in two application domains. Our experiments show that the designed agent is able to act within the set of behavior constraints without significantly degrading its overall reward performance.

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Zhou, Manli, Youxi Luo, Guoquan Sun, Guoqin Mai, and Fengfeng Zhou. "Constraint Programming Based Biomarker Optimization." BioMed Research International 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/910515.

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Efficient and intuitive characterization of biological big data is becoming a major challenge for modern bio-OMIC based scientists. Interactive visualization and exploration of big data is proven to be one of the successful solutions. Most of the existing feature selection algorithms do not allow the interactive inputs from users in the optimizing process of feature selection. This study investigates this question as fixing a few user-input features in the finally selected feature subset and formulates these user-input features as constraints for a programming model. The proposed algorithm, fsCoP (feature selection based on constrained programming), performs well similar to or much better than the existing feature selection algorithms, even with the constraints from both literature and the existing algorithms. An fsCoP biomarker may be intriguing for further wet lab validation, since it satisfies both the classification optimization function and the biomedical knowledge. fsCoP may also be used for the interactive exploration of bio-OMIC big data by interactively adding user-defined constraints for modeling.

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Lan, Nanying, Fanchang Zhang, Kaipan Xiao, Heng Zhang, and Yuhan Lin. "Low-Dimensional Multi-Trace Impedance Inversion in Sparse Space with Elastic Half Norm Constraint." Minerals 13, no. 7 (July 22, 2023): 972. http://dx.doi.org/10.3390/min13070972.

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Recently, multi-trace impedance inversion has attracted great interest in seismic exploration because it improves the horizontal continuity and fidelity of the inversion results by exploiting the lateral structure information of the strata. However, computational inefficiency affects its practical application. Furthermore, in terms of vertical constraints on the model parameters, it only considers smooth features while ignoring sharp discontinuity features. This leads to yielding an over-smooth solution that does not accurately reflect the distribution of underground rock. To deal with the above situations, we first develop a low-dimensional multi-trace impedance inversion (LMII) framework. Inspired by compressed sensing, this framework utilizes low-dimensional measurements in sparse space containing the maximum information of the signal to construct the objective function for multi-trace inversion, which can significantly reduce the size of the inversion problem and improve the inverse efficiency. Then, we introduce the elastic half (EH) norm as a vertical constraint on the model parameters in the LMII framework and formulate a novel constrained LMII model for impedance inversion. Because the introduced EH norm takes into account both the smoothness and blockiness of rock impedance, the constrained LMII model can effectively raise the inversion accuracy of complex strata. Finally, an efficient alternating multiplier iteration algorithm is derived based on the variable splitting technique to optimize the constrained LMII model. The performance of the developed approaches is tested using synthetic and practical data, and the results prove their feasibility and superiority.

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Ayton, Benjamin, Brian Williams, and Richard Camilli. "Measurement Maximizing Adaptive Sampling with Risk Bounding Functions." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7511–19. http://dx.doi.org/10.1609/aaai.v33i01.33017511.

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In autonomous exploration a mobile agent must adapt to new measurements to seek high reward, but disturbances cause a probability of collision that must be traded off against expected reward. This paper considers an autonomous agent tasked with maximizing measurements from a Gaussian Process while subject to unbounded disturbances. We seek an adaptive policy in which the maximum allowed probability of failure is constrained as a function of the expected reward. The policy is found using an extension to Monte Carlo Tree Search (MCTS) which bounds probability of failure. We apply MCTS to a sequence of approximating problems, which allows constraint satisfying actions to be found in an anytime manner. Our innovation lies in defining the approximating problems and replanning strategy such that the probability of failure constraint is guaranteed to be satisfied over the true policy. The approach does not need to plan for all measurements explicitly or constrain planning based only on the measurements that were observed. To the best of our knowledge, our approach is the first to enforce probability of failure constraints in adaptive sampling. Through experiments on real bathymetric data and simulated measurements, we show our algorithm allows an agent to take dangerous actions only when the reward justifies the risk. We then verify through Monte Carlo simulations that failure bounds are satisfied.

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Duecker, Daniel Andre, Andreas Rene Geist, Edwin Kreuzer, and Eugen Solowjow. "Learning Environmental Field Exploration with Computationally Constrained Underwater Robots: Gaussian Processes Meet Stochastic Optimal Control." Sensors 19, no. 9 (May 6, 2019): 2094. http://dx.doi.org/10.3390/s19092094.

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Autonomous exploration of environmental fields is one of the most promising tasks to be performed by fleets of mobile underwater robots. The goal is to maximize the information gain during the exploration process by integrating an information-metric into the path-planning and control step. Therefore, the system maintains an internal belief representation of the environmental field which incorporates previously collected measurements from the real field. In contrast to surface robots, mobile underwater systems are forced to run all computations on-board due to the limited communication bandwidth in underwater domains. Thus, reducing the computational cost of field exploration algorithms constitutes a key challenge for in-field implementations on micro underwater robot teams. In this work, we present a computationally efficient exploration algorithm which utilizes field belief models based on Gaussian Processes, such as Gaussian Markov random fields or Kalman regression, to enable field estimation with constant computational cost over time. We extend the belief models by the use of weighted shape functions to directly incorporate spatially continuous field observations. The developed belief models function as information-theoretic value functions to enable path planning through stochastic optimal control with path integrals. We demonstrate the efficiency of our exploration algorithm in a series of simulations including the case of a stationary spatio-temporal field.

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Yasojima, Edson Koiti Kudo, Roberto Célio Limão de Oliveira, Otávio Noura Teixeira, and Rodrigo Lisbôa Pereira. "CAM-ADX: A New Genetic Algorithm with Increased Intensification and Diversification for Design Optimization Problems with Real Variables." Robotica 37, no. 9 (March 1, 2019): 1595–640. http://dx.doi.org/10.1017/s026357471900016x.

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SummaryThis paper presents a modified genetic algorithm (GA) using a new crossover operator (ADX) and a novel statistic correlation mutation algorithm (CAM). Both ADX and CAM work with population information to improve existing individuals of the GA and increase the exploration potential via the correlation mutation. Solution-based methods offer better local improvement of already known solutions while lacking at exploring the whole search space; in contrast, evolutionary algorithms provide better global search in exchange of exploitation power. Hybrid methods are widely used for constrained optimization problems due to increased global and local search capabilities. The modified GA improves results of constrained problems by balancing the exploitation and exploration potential of the algorithm. The conducted tests present average performance for various CEC’2015 benchmark problems, while offering better reliability and superior results on path planning problem for redundant manipulator and most of the constrained engineering design problems tested compared with current works in the literature and classic optimization algorithms.

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Molina, Anton, Shailabh Kumar, Stefan Karpitschka, and Manu Prakash. "Droplet tilings for rapid exploration of spatially constrained many-body systems." Proceedings of the National Academy of Sciences 118, no. 34 (August 20, 2021): e2020014118. http://dx.doi.org/10.1073/pnas.2020014118.

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Geometry in materials is a key concept which can determine material behavior in ordering, frustration, and fragmentation. More specifically, the behavior of interacting degrees of freedom subject to arbitrary geometric constraints has the potential to be used for engineering materials with exotic phase behavior. While advances in lithography have allowed for an experimental exploration of geometry on ordering that has no precedent in nature, many of these methods are low throughput or the underlying dynamics remain difficult to observe directly. Here, we introduce an experimental system that enables the study of interacting many-body dynamics by exploiting the physics of multidroplet evaporation subject to two-dimensional spatial constraints. We find that a high-energy initial state of this system settles into frustrated, metastable states with relaxation on two timescales. We understand this process using a minimal dynamical model that simulates the overdamped dynamics of motile droplets by identifying the force exerted on a given droplet as being proportional to the two-dimensional vapor gradients established by its neighbors. Finally, we demonstrate the flexibility of this platform by presenting experimental realizations of droplet−lattice systems representing different spin degrees of freedom and lattice geometries. Our platform enables a rapid and low-cost means to directly visualize dynamics associated with complex many-body systems interacting via long-range interactions. More generally, this platform opens up the rich design space between geometry and interactions for rapid exploration with minimal resources.

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Zhao, Zhuoran, Kamyar Mirzazad Barijough, and Andreas Gerstlauer. "Network-level Design Space Exploration of Resource-constrained Networks-of-Systems." ACM Transactions on Embedded Computing Systems 19, no. 4 (July 16, 2020): 1–26. http://dx.doi.org/10.1145/3387918.

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Adamatzky, Andrew. "On exploration of geometrically constrained space by medicinal leeches Hirudo verbana." Biosystems 130 (April 2015): 28–36. http://dx.doi.org/10.1016/j.biosystems.2015.02.005.

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Ali, M. M., and Z. Kajee-Bagdadi. "A local exploration-based differential evolution algorithm for constrained global optimization." Applied Mathematics and Computation 208, no. 1 (February 2009): 31–48. http://dx.doi.org/10.1016/j.amc.2008.11.036.

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Robertson, Scott P., David Koizumi, and Stacy C. Marsella. "Constraints on Training: Informativeness and Breadth in Procedural Skill Learning." Proceedings of the Human Factors Society Annual Meeting 32, no. 5 (October 1988): 377–80. http://dx.doi.org/10.1177/154193128803200532.

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Three training methods were compared in a computer text editing situation. Training varied on the degree of constraint imposed on the behavior of learners. For complex methods, completely constrained and completely unconstrained training situations led to worse test perfomance than an unconstrianed training condition in which subjects were informed about their compliance with a method and allowed to reset the system at will. The results argue against a strict “training wheels” approach to learning environments but support the general notion of “guided exploration,” especially for complex methods.

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Cazzato, Dario, Pierluigi Carcagnì, Claudio Cimarelli, Holger Voos, Cosimo Distante, and Marco Leo. "Ocular Biometrics Recognition by Analyzing Human Exploration during Video Observations." Applied Sciences 10, no. 13 (June 30, 2020): 4548. http://dx.doi.org/10.3390/app10134548.

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Soft biometrics provide information about the individual but without the distinctiveness and permanence able to discriminate between any two individuals. Since the gaze represents one of the most investigated human traits, works evaluating the feasibility of considering it as a possible additional soft biometric trait have been recently appeared in the literature. Unfortunately, there is a lack of systematic studies on clinically approved stimuli to provide evidence of the correlation between exploratory paths and individual identities in “natural” scenarios (without calibration, imposed constraints, wearable tools). To overcome these drawbacks, this paper analyzes gaze patterns by using a computer vision based pipeline in order to prove the correlation between visual exploration and user identity. This correlation is robustly computed in a free exploration scenario, not biased by wearable devices nor constrained to a prior personalized calibration. Provided stimuli have been designed by clinical experts and then they allow better analysis of human exploration behaviors. In addition, the paper introduces a novel public dataset that provides, for the first time, images framing the faces of the involved subjects instead of only their gaze tracks.

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Bouskela, Adrien, Alexandre Kling, Tristan Schuler, Sergey Shkarayev, Himangshu Kalita, and Jekan Thangavelautham. "Mars Exploration Using Sailplanes." Aerospace 9, no. 6 (June 3, 2022): 306. http://dx.doi.org/10.3390/aerospace9060306.

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We present the preliminary design of sailplanes, used for Mars exploration. The sailplanes mitigate the weight and energy storage limitations traditionally associated with powered flight by instead exploiting atmospheric wind gradients for dynamic soaring, and slope/thermal updrafts for static soaring. Equations of motion for the sailplanes were combined with wind profiles from the Mars Regional Atmospheric Modeling System (MRAMS) for two representative sites: Jezero crater, Perseverance’s landing site, and over a section of the Valles Marineris canyon. Optimal flight trajectories were obtained from the constrained optimization problem, using the lift coefficient and the roll angle as control parameters. Numerical results for complete dynamic soaring cycles demonstrated that the total sailplane energy at the end of a soaring cycle increases by 6.8–11%. The absence of a propulsion system, allowing for a compact form factor, means the sailplanes can be packaged into CubeSats and deployed as secondary payloads at a relatively low cost; providing scientific data over locations inaccessible by current landers and rovers. Various sailplane deployment methods are considered, including rapid deployment during Entry, Descent, and Landing (EDL) of a Mars Science Laboratory-class (MSL) vehicle and slow deployment using a blimp.

Journal articles on the topic 'Constrained exploration'

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