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1
Mazzi, Giulio, Alberto Castellini, and Alessandro Farinelli. "Rule-based Shielding for Partially Observable Monte-Carlo Planning." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 243–51. http://dx.doi.org/10.1609/icaps.v31i1.15968.
Повний текст джерелаАнотація:
Partially Observable Monte-Carlo Planning (POMCP) is a powerful online algorithm able to generate approximate policies for large Partially Observable Markov Decision Processes. The online nature of this method supports scalability by avoiding complete policy representation. The lack of an explicit representation however hinders policy interpretability and makes policy verification very complex. In this work, we propose two contributions. The first is a method for identifying unexpected actions selected by POMCP with respect to expert prior knowledge of the task. The second is a shielding approach that prevents POMCP from selecting unexpected actions. The first method is based on Satisfiability Modulo Theory (SMT). It inspects traces (i.e., sequences of belief-action-observation triplets) generated by POMCP to compute the parameters of logical formulas about policy properties defined by the expert. The second contribution is a module that uses online the logical formulas to identify anomalous actions selected by POMCP and substitute those actions with actions that satisfy the logical formulas fulfilling expert knowledge. We evaluate our approach on Tiger, a standard benchmark for POMDPs, and a real-world problem related to mobile robot navigation. Results show that the shielded POMCP outperforms the standard POMCP in a case study in which a wrong parameter of POMCP makes it select wrong actions from time to time. Moreover, we show that the approach keeps good performance also if the parameters of the logical formula are optimized using trajectories containing some wrong actions.
2
Zhang, Zongzhang, Michael Littman, and Xiaoping Chen. "Covering Number as a Complexity Measure for POMDP Planning and Learning." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 1853–59. http://dx.doi.org/10.1609/aaai.v26i1.8360.
Повний текст джерелаАнотація:
Finding a meaningful way of characterizing the difficulty of partially observable Markov decision processes (POMDPs) is a core theoretical problem in POMDP research. State-space size is often used as a proxy for POMDP difficulty, but it is a weak metric at best. Existing work has shown that the covering number for the reachable belief space, which is a set of belief points that are reachable from the initial belief point, has interesting links with the complexity of POMDP planning, theoretically. In this paper, we present empirical evidence that the covering number for the reachable belief space (or just ``covering number", for brevity) is a far better complexity measure than the state-space size for both planning and learning POMDPs on several small-scale benchmark problems. We connect the covering number to the complexity of learning POMDPs by proposing a provably convergent learning algorithm for POMDPs without reset given knowledge of the covering number.
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Omidshafiei, Shayegan, Ali–Akbar Agha–Mohammadi, Christopher Amato, Shih–Yuan Liu, Jonathan P. How, and John Vian. "Decentralized control of multi-robot partially observable Markov decision processes using belief space macro-actions." International Journal of Robotics Research 36, no. 2 (February 2017): 231–58. http://dx.doi.org/10.1177/0278364917692864.
Повний текст джерелаАнотація:
This work focuses on solving general multi-robot planning problems in continuous spaces with partial observability given a high-level domain description. Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) are general models for multi-robot coordination problems. However, representing and solving Dec-POMDPs is often intractable for large problems. This work extends the Dec-POMDP model to the Decentralized Partially Observable Semi-Markov Decision Process (Dec-POSMDP) to take advantage of the high-level representations that are natural for multi-robot problems and to facilitate scalable solutions to large discrete and continuous problems. The Dec-POSMDP formulation uses task macro-actions created from lower-level local actions that allow for asynchronous decision-making by the robots, which is crucial in multi-robot domains. This transformation from Dec-POMDPs to Dec-POSMDPs with a finite set of automatically-generated macro-actions allows use of efficient discrete-space search algorithms to solve them. The paper presents algorithms for solving Dec-POSMDPs, which are more scalable than previous methods since they can incorporate closed-loop belief space macro-actions in planning. These macro-actions are automatically constructed to produce robust solutions. The proposed algorithms are then evaluated on a complex multi-robot package delivery problem under uncertainty, showing that our approach can naturally represent realistic problems and provide high-quality solutions for large-scale problems.
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Ye, Nan, Adhiraj Somani, David Hsu, and Wee Sun Lee. "DESPOT: Online POMDP Planning with Regularization." Journal of Artificial Intelligence Research 58 (January 26, 2017): 231–66. http://dx.doi.org/10.1613/jair.5328.
Повний текст джерелаАнотація:
The partially observable Markov decision process (POMDP) provides a principled general framework for planning under uncertainty, but solving POMDPs optimally is computationally intractable, due to the "curse of dimensionality" and the "curse of history". To overcome these challenges, we introduce the Determinized Sparse Partially Observable Tree (DESPOT), a sparse approximation of the standard belief tree, for online planning under uncertainty. A DESPOT focuses online planning on a set of randomly sampled scenarios and compactly captures the "execution" of all policies under these scenarios. We show that the best policy obtained from a DESPOT is near-optimal, with a regret bound that depends on the representation size of the optimal policy. Leveraging this result, we give an anytime online planning algorithm, which searches a DESPOT for a policy that optimizes a regularized objective function. Regularization balances the estimated value of a policy under the sampled scenarios and the policy size, thus avoiding overfitting. The algorithm demonstrates strong experimental results, compared with some of the best online POMDP algorithms available. It has also been incorporated into an autonomous driving system for real-time vehicle control. The source code for the algorithm is available online.
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Chatterjee, Krishnendu, Martin Chmelik, and Ufuk Topcu. "Sensor Synthesis for POMDPs with Reachability Objectives." Proceedings of the International Conference on Automated Planning and Scheduling 28 (June 15, 2018): 47–55. http://dx.doi.org/10.1609/icaps.v28i1.13875.
Повний текст джерелаАнотація:
Partially observable Markov decision processes (POMDPs) are widely used in probabilistic planning problems in which an agent interacts with an environment using noisy and imprecise sensors. We study a setting in which the sensors are only partially defined and the goal is to synthesize “weakest” additional sensors, such that in the resulting POMDP, there is a small-memory policy for the agent that almost-surely (with probability 1) satisfies a reachability objective. We show that the problem is NP-complete, and present a symbolic algorithm by encoding the problem into SAT instances. We illustrate trade-offs between the amount of memory of the policy and the number of additional sensors on a simple example. We have implemented our approach and consider three classical POMDP examples from the literature, and show that in all the examples the number of sensors can be significantly decreased (as compared to the existing solutions in the literature) without increasing the complexity of the policies.
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NI, YAODONG, and ZHI-QIANG LIU. "BOUNDED-PARAMETER PARTIALLY OBSERVABLE MARKOV DECISION PROCESSES: FRAMEWORK AND ALGORITHM." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 21, no. 06 (December 2013): 821–63. http://dx.doi.org/10.1142/s0218488513500396.
Повний текст джерелаАнотація:
Partially observable Markov decision processes (POMDPs) are powerful for planning under uncertainty. However, it is usually impractical to employ a POMDP with exact parameters to model the real-life situation precisely, due to various reasons such as limited data for learning the model, inability of exact POMDPs to model dynamic situations, etc. In this paper, assuming that the parameters of POMDPs are imprecise but bounded, we formulate the framework of bounded-parameter partially observable Markov decision processes (BPOMDPs). A modified value iteration is proposed as a basic strategy for tackling parameter imprecision in BPOMDPs. In addition, we design the UL-based value iteration algorithm, in which each value backup is based on two sets of vectors called U-set and L-set. We propose four strategies for computing U-set and L-set. We analyze theoretically the computational complexity and the reward loss of the algorithm. The effectiveness and robustness of the algorithm are shown empirically.
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Spaan, M. T. J., and N. Vlassis. "Perseus: Randomized Point-based Value Iteration for POMDPs." Journal of Artificial Intelligence Research 24 (August 1, 2005): 195–220. http://dx.doi.org/10.1613/jair.1659.
Повний текст джерелаАнотація:
Partially observable Markov decision processes (POMDPs) form an attractive and principled framework for agent planning under uncertainty. Point-based approximate techniques for POMDPs compute a policy based on a finite set of points collected in advance from the agent's belief space. We present a randomized point-based value iteration algorithm called Perseus. The algorithm performs approximate value backup stages, ensuring that in each backup stage the value of each point in the belief set is improved; the key observation is that a single backup may improve the value of many belief points. Contrary to other point-based methods, Perseus backs up only a (randomly selected) subset of points in the belief set, sufficient for improving the value of each belief point in the set. We show how the same idea can be extended to dealing with continuous action spaces. Experimental results show the potential of Perseus in large scale POMDP problems.
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Amato, Christopher, George Konidaris, Ariel Anders, Gabriel Cruz, Jonathan P. How, and Leslie P. Kaelbling. "Policy search for multi-robot coordination under uncertainty." International Journal of Robotics Research 35, no. 14 (December 2016): 1760–78. http://dx.doi.org/10.1177/0278364916679611.
Повний текст джерелаАнотація:
We introduce a principled method for multi-robot coordination based on a general model (termed a MacDec-POMDP) of multi-robot cooperative planning in the presence of stochasticity, uncertain sensing, and communication limitations. A new MacDec-POMDP planning algorithm is presented that searches over policies represented as finite-state controllers, rather than the previous policy tree representation. Finite-state controllers can be much more concise than trees, are much easier to interpret, and can operate over an infinite horizon. The resulting policy search algorithm requires a substantially simpler simulator that models only the outcomes of executing a given set of motor controllers, not the details of the executions themselves and can solve significantly larger problems than existing MacDec-POMDP planners. We demonstrate significant performance improvements over previous methods and show that our method can be used for actual multi-robot systems through experiments on a cooperative multi-robot bartending domain.
9
Pineau, J., G. Gordon, and S. Thrun. "Anytime Point-Based Approximations for Large POMDPs." Journal of Artificial Intelligence Research 27 (November 26, 2006): 335–80. http://dx.doi.org/10.1613/jair.2078.
Повний текст джерелаАнотація:
The Partially Observable Markov Decision Process has long been recognized as a rich framework for real-world planning and control problems, especially in robotics. However exact solutions in this framework are typically computationally intractable for all but the smallest problems. A well-known technique for speeding up POMDP solving involves performing value backups at specific belief points, rather than over the entire belief simplex. The efficiency of this approach, however, depends greatly on the selection of points. This paper presents a set of novel techniques for selecting informative belief points which work well in practice. The point selection procedure is combined with point-based value backups to form an effective anytime POMDP algorithm called Point-Based Value Iteration (PBVI). The first aim of this paper is to introduce this algorithm and present a theoretical analysis justifying the choice of belief selection technique. The second aim of this paper is to provide a thorough empirical comparison between PBVI and other state-of-the-art POMDP methods, in particular the Perseus algorithm, in an effort to highlight their similarities and differences. Evaluation is performed using both standard POMDP domains and realistic robotic tasks.
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Wu, Chenyang, Rui Kong, Guoyu Yang, Xianghan Kong, Zongzhang Zhang, Yang Yu, Dong Li, and Wulong Liu. "LB-DESPOT: Efficient Online POMDP Planning Considering Lower Bound in Action Selection (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 15927–28. http://dx.doi.org/10.1609/aaai.v35i18.17960.
Повний текст джерелаАнотація:
Partially observable Markov decision process (POMDP) is an extension to MDP. It handles the state uncertainty by specifying the probability of getting a particular observation given the current state. DESPOT is one of the most popular scalable online planning algorithms for POMDPs, which manages to significantly reduce the size of the decision tree while deriving a near-optimal policy by considering only $K$ scenarios. Nevertheless, there is a gap in action selection criteria between planning and execution in DESPOT. During the planning stage, it keeps choosing the action with the highest upper bound, whereas when the planning ends, the action with the highest lower bound is chosen for execution. Here, we propose LB-DESPOT to alleviate this issue, which utilizes the lower bound in selecting an action branch to expand. Empirically, our method has attained better performance than DESPOT and POMCP, which is another state-of-the-art, on several challenging POMDP benchmark tasks.
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Nguyen, Hoa Van, Hamid Rezatofighi, Ba-Ngu Vo, and Damith C. Ranasinghe. "Multi-Objective Multi-Agent Planning for Jointly Discovering and Tracking Mobile Objects." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7227–35. http://dx.doi.org/10.1609/aaai.v34i05.6213.
Повний текст джерелаАнотація:
We consider the challenging problem of online planning for a team of agents to autonomously search and track a time-varying number of mobile objects under the practical constraint of detection range limited onboard sensors. A standard POMDP with a value function that either encourages discovery or accurate tracking of mobile objects is inadequate to simultaneously meet the conflicting goals of searching for undiscovered mobile objects whilst keeping track of discovered objects. The planning problem is further complicated by misdetections or false detections of objects caused by range limited sensors and noise inherent to sensor measurements. We formulate a novel multi-objective POMDP based on information theoretic criteria, and an online multi-object tracking filter for the problem. Since controlling multi-agent is a well known combinatorial optimization problem, assigning control actions to agents necessitates a greedy algorithm. We prove that our proposed multi-objective value function is a monotone submodular set function; consequently, the greedy algorithm can achieve a (1-1/e) approximation for maximizing the submodular multi-objective function.
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Beynier, Aurélie. "A Multiagent Planning Approach for Cooperative Patrolling with Non-Stationary Adversaries." International Journal on Artificial Intelligence Tools 26, no. 05 (October 2017): 1760018. http://dx.doi.org/10.1142/s0218213017600181.
Повний текст джерелаАнотація:
Multiagent patrolling is the problem faced by a set of agents that have to visit a set of sites to prevent or detect some threats or illegal actions. Although it is commonly assumed that patrollers share a common objective, the issue of cooperation between the patrollers has received little attention. Over the last years, the focus has been put on patrolling strategies to prevent a one-shot attack from an adversary. This adversary is usually assumed to be fully rational and to have full observability of the system. Most approaches are then based on game theory and consists in computing a best response strategy. Nonetheless, when patrolling frontiers, detecting illegal fishing or poaching; patrollers face multiple adversaries with limited observability and rationality. Moreover, adversaries can perform multiple illegal actions over time and space and may change their strategies as time passes. In this paper, we propose a multiagent planning approach that enables effective cooperation between a team of patrollers in uncertain environments. Patrolling agents are assumed to have partial observability of the system. Our approach allows the patrollers to learn a generic and stochastic model of the adversaries based on the history of observations. A wide variety of adversaries can thus be considered with strategies ranging from random behaviors to fully rational and informed behaviors. We show that the multiagent planning problem can be formalized by a non-stationary DEC- POMDP. In order to deal with the non-stationary, we introduce the notion of context. We then describe an evolutionary algorithm to compute patrolling strategies on-line, and we propose methods to improve the patrollers’ performance.
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Ivashko, Yulia, Andrii Dmytrenko, Małgorzata Hryniewicz, Tetiana Petrunok, and Tеtiana Yevdokimova. ""Official" and "private" parks of the XVIII–XIX centuries through the prism of general landscape trends of the time." Landscape architecture and art, no. 20 (November 10, 2022): 24–36. http://dx.doi.org/10.22616/j.landarchart.2022.20.03.
Повний текст джерелаАнотація:
The article analyzes the basic principles of landscape design of the imperial and aristocratic parks in the Russian Empire in the XVIII–XIX centuries. There were "official" parks designed to be visited by high-ranking guests, and "private" parks, which were not covered by the canons of the "official" park. In the Tsarskoye Selo imperial residence Catherine's Park performed the function of "official" with the appropriate function of pomp, and located next to it Alexander's Park –respectively, the function of "private" imperial park. Catherine's Park became a model to follow one of the mostfamous parks in modern Ukraine –Oleksandriia Park in the city of Bila Tserkva. The common and different between Tsarskoye Selo park residence and aristocratic parks in Ukraine are analyzed, the principles of planning of these parks and the main constituent elements are compared. Based on this, the basic principles of planning parks of the Classicism and Empire style era in Ukraine and the "iconic" set of pavilions are determined. The general canons of the "Ossian Park" and their specific embodiment are analyzed on the example of Sofiivka Park in Uman. It was determined that the "Ossian Park" based on the canons is opposite to the parks of Classicism-Empire style.The methods of historical and culturological analysis, method of comparative analysis, method of field surveys used.
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Lauri, Mikko, Joni Pajarinen, and Jan Peters. "Multi-agent active information gathering in discrete and continuous-state decentralized POMDPs by policy graph improvement." Autonomous Agents and Multi-Agent Systems 34, no. 2 (June 10, 2020). http://dx.doi.org/10.1007/s10458-020-09467-6.
Повний текст джерелаАнотація:
Abstract Decentralized policies for information gathering are required when multiple autonomous agents are deployed to collect data about a phenomenon of interest when constant communication cannot be assumed. This is common in tasks involving information gathering with multiple independently operating sensor devices that may operate over large physical distances, such as unmanned aerial vehicles, or in communication limited environments such as in the case of autonomous underwater vehicles. In this paper, we frame the information gathering task as a general decentralized partially observable Markov decision process (Dec-POMDP). The Dec-POMDP is a principled model for co-operative decentralized multi-agent decision-making. An optimal solution of a Dec-POMDP is a set of local policies, one for each agent, which maximizes the expected sum of rewards over time. In contrast to most prior work on Dec-POMDPs, we set the reward as a non-linear function of the agents’ state information, for example the negative Shannon entropy. We argue that such reward functions are well-suited for decentralized information gathering problems. We prove that if the reward function is convex, then the finite-horizon value function of the Dec-POMDP is also convex. We propose the first heuristic anytime algorithm for information gathering Dec-POMDPs, and empirically prove its effectiveness by solving discrete problems an order of magnitude larger than previous state-of-the-art. We also propose an extension to continuous-state problems with finite action and observation spaces by employing particle filtering. The effectiveness of the proposed algorithms is verified in domains such as decentralized target tracking, scientific survey planning, and signal source localization.
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Vien, Ngo Anh, and Marc Toussaint. "Hierarchical Monte-Carlo Planning." Proceedings of the AAAI Conference on Artificial Intelligence 29, no. 1 (March 4, 2015). http://dx.doi.org/10.1609/aaai.v29i1.9687.
Повний текст джерелаАнотація:
Monte-Carlo Tree Search, especially UCT and its POMDP version POMCP, have demonstrated excellent performanceon many problems. However, to efficiently scale to large domains one should also exploit hierarchical structure if present. In such hierarchical domains, finding rewarded states typically requires to search deeply; covering enough such informative states very far from the root becomes computationally expensive in flat non-hierarchical search approaches. We propose novel, scalable MCTS methods which integrate atask hierarchy into the MCTS framework, specifically lead-ing to hierarchical versions of both, UCT and POMCP. The new method does not need to estimate probabilistic models of each subtask, it instead computes subtask policies purely sample-based. We evaluate the hierarchical MCTS methods on various settings such as a hierarchical MDP, a Bayesian model-based hierarchical RL problem, and a large hierarchical POMDP.
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Sheng, Shili, Erfan Pakdamanian, Kyungtae Han, Ziran Wang, John Lenneman, David Parker, and Lu Feng. "Planning for Automated Vehicles with Human Trust." ACM Transactions on Cyber-Physical Systems, September 2, 2022. http://dx.doi.org/10.1145/3561059.
Повний текст джерелаАнотація:
Recent work has considered personalized route planning based on user profiles, but none of it accounts for human trust. We argue that human trust is an important factor to consider when planning routes for automated vehicles. This paper presents a trust-based route planning approach for automated vehicles. We formalize the human-vehicle interaction as a partially observable Markov decision process (POMDP) and model trust as a partially observable state variable of the POMDP, representing the human’s hidden mental state. We build data-driven models of human trust dynamics and takeover decisions, which are incorporated in the POMDP framework, using data collected from an online user study with 100 participants on the Amazon Mechanical Turk platform. We compute optimal routes for automated vehicles by solving optimal policies in the POMDP planning, and evaluate the resulting routes via human subject experiments with 22 participants on a driving simulator. The experimental results show that participants taking the trust-based route generally reported more positive responses in the after-driving survey than those taking the baseline (trust-free) route. In addition, we analyze the trade-offs between multiple planning objectives (e.g., trust, distance, energy consumption) via multi-objective optimization of the POMDP. We also identify a set of open issues and implications for real-world deployment of the proposed approach in automated vehicles.