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Online Planning in POMDPs with Self-Improving Simulators [article]

Jinke He, Miguel Suau, Hendrik Baier, Michael Kaisers, Frans A. Oliehoek
2022
Experimental results in two large domains show that when integrated with POMCP, our approach allows to plan with improving efficiency over time.  ...  Given the original simulator of the environment, which may be computationally very demanding, we propose to learn online an approximate but much faster simulator that improves over time.  ...  3 Hyperparameters for planning with self-improving simulators in the grid traffic control domain.  ... 
doi:10.48550/arxiv.2201.11404 fatcat:3vs3bpvnzjgvpiv4b377jwykje

Closing the Planning-Learning Loop with Application to Autonomous Driving in a Crowd [article]

Panpan Cai, David Hsu
2021 arXiv   pre-print
The learned policy in turn guides online planning for real-time vehicle control.  ...  To contend with an uncertain, interactive environment with heterogeneous traffic of cars, motorcycles, buses, ..., the robot vehicle has to plan in both short and long terms in order to drive effectively  ...  Fig. 2 . 2 LeTS-Drive integrates online planning with self-supervised learning or reinforcement learning to close the planning-learning loop.  ... 
arXiv:2101.03834v2 fatcat:3rjws4a5h5grxf5rj7x6fxa34m

An Approach for Multi-UAV System Navigation and Target Finding in Cluttered Environments

Xiaolong Zhu, Fernando Vanegas, Felipe Gonzalez
2020 2020 International Conference on Unmanned Aircraft Systems (ICUAS)  
Hanna Kurniawati for the open source software TAPIR that was used as the online POMDP solver.  ...  the system performance, and incorporates an online POMDP solver as the motion planner of each UAV subsystem to calculate the policy with improved quality and a long horizon steps and with the shared processed  ...  Most of those works are simulated in a small discrete state space with limited actions by using multi-agent POMDP (MPOMDP) or Decentralised POMDP (Dec-POMDP) models [11] - [14] .  ... 
doi:10.1109/icuas48674.2020.9214062 fatcat:uj6jt7rgnbchxfpff6dppnndka

Knowledge Base K Models to Support Trade-Offs for Self-Adaptation using Markov Processes

Luis Hernan Garcia Paucar, Nelly Bencomo
2019 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)  
In this paper, we demonstrate a novel use of Partially Observable Markov Decision Processes (POMDPs) as runtime models to support the decision-making of a Self Adaptive System (SAS) in the context of the  ...  The trade-off between the non-functional requirements (NFRs) has been embodied as a POMDP in the context of the MAPE-K loop.  ...  Online POMDP planning is a technique that interleaves planning with plan execution: at each time slice, the system searches for an optimal action a ∈ A at the current belief b.  ... 
doi:10.1109/saso.2019.00011 dblp:conf/saso/PaucarB19 fatcat:knysm6t75rdjhgjhz7ro5lxrym

Scalable Planning and Learning for Multiagent POMDPs

Christopher Amato, Frans Oliehoek
2015 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Online, sample-based planning algorithms for POMDPs have shown great promise in scaling to problems with large state spaces, but they become intractable for large action and observation spaces.  ...  This is particularly problematic in multiagent POMDPs where the action and observation space grows exponentially with the number of agents.  ...  Introduction Online planning methods for POMDPs have demonstrated impressive performance (Ross et al. 2008) on large problems by interleaving planning with action selection.  ... 
doi:10.1609/aaai.v29i1.9439 fatcat:cbabc6a4u5exhhai2irdipuszy

PSINET: Assisting HIV Prevention Amongst Homeless Youth by Planning Ahead

A Yadav, L S Marcolino, E Rice, R Petering, H Winetrobe, H Rhoades, M Tambe, H Carmichael
2016 The AI Magazine  
PSINET was developed in collaboration with My Friend's Place (a drop-in agency serving homeless youth in Los Angeles) and is currently being reviewed by their officials.  ...  Simulations show that PSINET achieves ~60% more information spread over the current state-of-the-art.  ...  Thus, online planning interleaves planning and execution at every time step.  ... 
pmid:27642227 pmcid:PMC5020561 fatcat:ld6sqjbumncttnju3sgs5iecg4

HARPS: An Online POMDP Framework for Human-Assisted Robotic Planning and Sensing [article]

Luke Burks, Hunter M. Ray, Jamison McGinley, Sousheel Vunnam, Nisar Ahmed
2022 arXiv   pre-print
states for improved online planning.  ...  of online sampling-based POMDP policies, multimodal semantic interaction, and Bayesian data fusion.  ...  These features effectively enable online 'reprogramming' of uncertain POMDPs together with human-robot sensor fusion to support online re-planning in complex environments.  ... 
arXiv:2110.10324v3 fatcat:oigsnlqnwffk5ce2gwds57onvm

PSINET: Assisting HIV Prevention Amongst Homeless Youth by Planning Ahead

Amulya Yadav, Leandro Soriano Marcolino, Eric Rice, Robin Petering, Hailey Winetrobe, Harmony Rhoades, Milind Tambe, Heather Carmichael
2016 The AI Magazine  
PSINET was developed in collaboration with My Friend's Place (a drop-in agency serving homeless youth in Los Angeles) and is currently being reviewed by their officials.  ...  PSINET includes the following key novelties: (1) it handles uncertainties in network structure and evolving network state; (2) it addresses these uncertainties by using POMDPs in influence maximization  ...  Thus, online planning interleaves planning and execution at every time step.  ... 
doi:10.1609/aimag.v37i2.2632 fatcat:vo5ewau4ifbbzdwn6wmezmv65a

Scalable Planning and Learning for Multiagent POMDPs: Extended Version [article]

Christopher Amato, Frans A. Oliehoek
2014 arXiv   pre-print
Online, sample-based planning algorithms for POMDPs have shown great promise in scaling to problems with large state spaces, but they become intractable for large action and observation spaces.  ...  This is particularly problematic in multiagent POMDPs where the action and observation space grows exponentially with the number of agents.  ...  Introduction Online planning methods for POMDPs have demonstrated impressive performance (Ross et al., 2008) on large problems by interleaving planning with action selection.  ... 
arXiv:1404.1140v2 fatcat:rt5w7oxourd4xbx6h4ngv27u44

DESPOT: Online POMDP Planning with Regularization

Nan Ye, Adhiraj Somani, David Hsu, Wee Sun Lee
2017 The Journal of Artificial Intelligence Research  
The algorithm demonstrates strong experimental results, compared with some of the best online POMDP algorithms available.  ...  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  ...  We are grateful to the anonymous reviewers for carefully reading the manuscript and providing many suggestions which helped greatly in improving the paper.  ... 
doi:10.1613/jair.5328 fatcat:rk7kxw64lzccfd5rvbo3enkjnu

POMP++: Pomcp-based Active Visual Search in unknown indoor environments [article]

Francesco Giuliari, Alberto Castellini, Riccardo Berra, Alessio Del Bue, Alessandro Farinelli, Marco Cristani, Francesco Setti, Yiming Wang
2021 arXiv   pre-print
In this paper we focus on the problem of learning online an optimal policy for Active Visual Search (AVS) of objects in unknown indoor environments.  ...  We propose POMP++, a planning strategy that introduces a novel formulation on top of the classic Partially Observable Monte Carlo Planning (POMCP) framework, to allow training-free online policy learning  ...  We formulate the AVS problem as a POMDP and employ POMCP to compute the planning policy online.  ... 
arXiv:2107.00914v2 fatcat:u2edvnoapfgppcewu7xc5fi3cu

Optimal Continuous State POMDP Planning with Semantic Observations: A Variational Approach [article]

Luke Burks, Ian Loefgren, Nisar Ahmed
2019 arXiv   pre-print
This work develops novel strategies for optimal planning with semantic observations using continuous state partially observable markov decision processes (CPOMDPs).  ...  Simulation results for a target search and interception task with semantic observations show that the GM policies resulting from these innovations are more effective than those produced by other state  ...  CNS-1650468 along with significant contributions from C-UAS industry members.  ... 
arXiv:1807.08229v2 fatcat:wtm67jiqxfdl5irtijq5iunhsm

Conflicts in teamwork

M. Tambe, J. P. Pearce, P. Paruchuri, D. Pynadath, P. Scerri, N. Schurr, P. Varakantham, E. Bowring, H. Jung, G. Kaminka, R. Maheswaran, J. Marecki (+3 others)
2005 Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems - AAMAS '05  
For example, in the BDI-POMDP hybrid approach, BDI team plans are exploited to improve POMDP tractability, and POMDPs improve BDI team plan performance.  ...  We present some recent results from applying this approach in a Disaster Rescue simulation domain being developed with help from the Los Angeles Fire Department.  ...  Fortunately, with the BDI-POMDP hybrid approach, BDI team plans are exploited to improve POMDP tractability, and POMDPs improve BDI team plan performance.  ... 
doi:10.1145/1082473.1082474 dblp:conf/atal/TambeBJKMMMNOPPPSSV05 fatcat:kjvy6vfd2jd3xo4kopie5irlbu

Active Inference Tree Search in Large POMDPs [article]

Domenico Maisto, Francesco Gregoretti, Karl Friston, Giovanni Pezzulo
2022 arXiv   pre-print
Model-based planning and prospection are widely studied in both cognitive neuroscience and artificial intelligence (AI), but from different perspectives--and with different desiderata in mind (biological  ...  Here, we introduce a novel method to plan in large POMDPs--Active Inference Tree Search (AcT)--that combines the normative character and biological realism of a leading planning theory in neuroscience  ...  The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.  ... 
arXiv:2103.13860v2 fatcat:yhmp4aghpvamhnnkomecw6qcii

Artificial Intelligence for Low-Resource Communities: Influence Maximization in an Uncertain World [article]

Amulya Yadav
2019 arXiv   pre-print
Our results show that our AI algorithms improved upon the state-of-the-art by 160% in the real-world.  ...  These algorithms utilize techniques from sequential planning problems and social network theory to develop new kinds of AI algorithms.  ...  Online POMDP Planning In the paradigm of online POMDP planning, instead of computing the entire POMDP policy, only the best action for the current belief state is found.  ... 
arXiv:1912.02102v1 fatcat:rtpw6mmupncydpxsw2b6q2lvra
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