@article{lovejoyComputationallyFeasibleBounds1991,
	title = {Computationally Feasible Bounds for Partially Observed {Markov} Decision Processes},
	volume = {39},
	abstract = {A partially observed Markov decision process (POMDP) is a sequential decision problem where information concerning parameters of interest is incomplete, and possible actions include sampling, surveying, or otherwise collecting additional information. Such problems can theoretically be solved as dynamic programs, but the relevant state space is infinite, which inhibits algorithmic solution. This paper explains how to approximate the state space by a finite grid of points, and use that grid to construct upper and lower value function bounds, generate approximate nonstationary and stationary policies, and bound the value loss relative to optimal for using these policies in the decision problem. A numerical example illustrates the methodology.},
	number = {1},
	urldate = {2020-12-30},
	journal = {Operations Research},
	author = {Lovejoy, William S.},
	year = {1991},
	pages = {162--175},
}

@inproceedings{brafmanHeuristicVariableGrid1997,
	title = {A heuristic variable grid solution method for {POMDPs}},
	abstract = {Partially observable Markov decision processes (POMDPs) are an appealing tool for modeling planning problems under uncertainty. They incorporate stochastic action and sensor descriptions and easily capture goal oriented and process onented tasks. Unfortunately, POMDPs are very difficult to solve. Exact methods cannot handle problems with much more than 10 states, so approximate methods must be used. In this paper, we describe a simple variable-grid solution method which yields good results on relatively large problems with modest computational effort.},
	urldate = {2021-01-03},
	booktitle = {Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on {Innovative} applications of artificial intelligence},
	author = {Brafman, Ronen I.},
	year = {1997},
	pages = {727--733},
	file = {AAAI97-113.pdf:C\:\\Users\\mana_\\Zotero\\storage\\JZAMTSX9\\AAAI97-113.pdf:application/pdf},
}

@article{hauskrechtValuefunctionApproximationsPartially2000,
	title = {Value-function approximations for partially observable {Markov} decision processes},
	volume = {13},
	number = {1},
	journal = {Journal of Artificial Intelligence Research},
	author = {Hauskrecht, Milos},
	year = {2000},
	pages = {33--94},
}

@inproceedings{zhouImprovedGridbasedApproximation2001,
	title = {An improved grid-based approximation algorithm for {POMDPs}},
	urldate = {2020-12-30},
	booktitle = {Proceedings of the 17th international joint conference on {Artificial} intelligence - {Volume} 1},
	author = {Zhou, Rong and Hansen, Eric A.},
	year = {2001},
	pages = {707--714},
}

@inproceedings{bonetEpsilonOptimalGridBasedAlgorithm2002,
	title = {An epsilon-Optimal Grid-Based Algorithm for Partially Observable {Markov} Decision Processes},
	abstract = {Wepresentan †-optimalgridbasedalgorithm for pomdps that is tractable in †¡1, the dis- countfactorandthemaximumabsolutevalue of the cost function, but exponential in the dimension of the state space. To the best of our knowledge, this is the flrst optimal grid-based algorithm for pomdps: all other optimal algorithms that we know are based onSondik'srepresentationoftheValueFunc- tion. We also propose a robustness criterion for grid-based algorithms and show that the new algorithm is robust in such sense.},
	author = {Bonet, Blai},
	year = {2002},
	pages = {51--58},
    booktitle = {Proceedings of the Nineteenth International Conference on Machine Learning},
}

@inproceedings{rachelsonLocalityActionDomination2010,
  title = {On the Locality of Action Domination in Sequential Decision Making},
  booktitle = {Proceedings of the {{International Symposium}} on {{Artificial Intelligence}} and {{Mathematics}}},
  author = {Rachelson, E. and Lagoudakis, M.},
  year = {2010},
  urldate = {2023-10-25},
  abstract = {In the field of sequential decision making and reinforcement learning, it has been observed that good policies for most problems exhibit a significant amount of structure. In practice, this implies that when a learning agent discovers an action is better than any other in a given state, this action actually happens to also dominate in a certain neighbourhood around that state. This paper presents new results proving that this notion of locality in action domination can be linked to the smoothness of the environment's underlying stochastic model. Namely, we link the Lipschitz continuity of a Markov Decision Process to the Lispchitz continuity of its policies' value functions and introduce the key concept of influence radius to describe the neighbourhood of states where the dominating action is guaranteed to be constant. These ideas are directly exploited into the proposed Localized Policy Iteration (LPI) algorithm, which is an active learning version of Rollout-based Policy Iteration. Preliminary results on the Inverted Pendulum domain demonstrate the viability and the potential of the proposed approach.},
  file = {C:\Users\mana_\Zotero\storage\DQHIG7MZ\Rachelson and Lagoudakis - 2010 - On the locality of action domination in sequential.pdf}
}




@inproceedings{miuraUnifyingFrameworkObserveraware2021,
	title = {A unifying framework for observer-aware planning and its complexity},
	booktitle = {Uncertainty in {Artificial} {Intelligence}},
	author = {Miura, Shuwa and Zilberstein, Shlomo},
	year = {2021},
	pages = {610--620},
}

@book{bertsekasDynamicProgrammingOptimal2012a,
	title = {Dynamic {Programming} and {Optimal} {Control}: {Volume} {II}; {Approximate} {Dynamic} {Programming}},
	isbn = {978-1-886529-44-1},
	shorttitle = {Dynamic {Programming} and {Optimal} {Control}},
	abstract = {This is the leading and most up-to-date textbook on the far-ranging algorithmic methodology of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization. The treatment focuses on basic unifying themes, and conceptual foundations. It illustrates the versatility, power, and generality of the method with many examples and applications from engineering, operations research, and other fields. It also addresses extensively the practical application of the methodology, possibly through the use of approximations, and provides an extensive treatment of the far-reaching methodology of Neuro-Dynamic Programming/Reinforcement Learning.Among its special features, the book 1) provides a unifying framework for sequential decision making, 2) treats simultaneously deterministic and stochastic control problems popular in modern control theory and Markovian decision popular in operations research, 3) develops the theory of deterministic optimal control problems including the Pontryagin Minimum Principle, 4) introduces recent suboptimal control and simulation-based approximation techniques (neuro-dynamic programming), which allow the practical application of dynamic programming to complex problems that involve the dual curse of large dimension and lack of an accurate mathematical model, 5) provides a comprehensive treatment of infinite horizon problems in the second volume, and an introductory treatment in the first volume.},
	language = {en},
	publisher = {Athena Scientific},
	author = {Bertsekas, Dimitri},
	month = oct,
	year = {2012},
	note = {Google-Books-ID: C1JEEAAAQBAJ},
	keywords = {Mathematics / Optimization},
}


@techreport{bonetSolvingStochasticShortestPath2002,
	title = {Solving Stochastic Shortest-Path Problems with {RTDP}},
	abstract = {We present a modification of the Real-Time Dynamic Programming (rtdp) algorithm that makes it a genuine off-line algorithm for solving Stochastic Shortest-Path problems. Also, a new domainindependent and admissible heuristic is presented for Stochastic Shortest-Path problems. The new algorithm and heuristic are compared with Value Iteration over benchmark problems with large state spaces. The results show that the modified rtdp algorithm can beat standard Value Iteration by several orders of magnitude in problems with large state space.},
	author = {Bonet, Blai and Geffner, Hector},
	year = {2002},
    institution = {Universidad Simon Bolivar}

}


@inproceedings{bonetSolvingPOMDPsRTDPbel2009,
	title = {Solving {POMDPs}: {RTDP}-bel vs. point-based algorithms},
	shorttitle = {Solving {POMDPs}},
	urldate = {2023-11-28},
	booktitle = {Proceedings of the 21st {International} {Joint} {Conference} on {Artificial} {Intelligence}},
	author = {Bonet, Blai and Geffner, Héctor},
	year = {2009},
	pages = {1641--1646},
}

@inproceedings{miuraMaximizingLegibilityStochastic2021,
	title = {Maximizing Legibility in Stochastic Environments},
	urldate = {2023-10-23},
	booktitle = {2021 30th {IEEE} {International} {Conference} on {Robot} \& {Human} {Interactive} {Communication}},
	author = {Miura, Shuwa and Cohen, Andrew L. and Zilberstein, Shlomo},
	year = {2021},
	pages = {1053--1059},
}

@inproceedings{fisacGeneratingPlansThat2020,
	title = {Generating Plans that Predict Themselves},
	abstract = {Collaboration requires coordination, and we coordinate by anticipating our teammates’ future actions and adapting to their plan. In some cases, our teammates’ actions early on can give us a clear idea of what the remainder of their plan is, i.e. what action sequence we should expect. In others, they might leave us less confident, or even lead us to the wrong conclusion. Our goal is for robot actions to fall in the first category: we want to enable robots to select their actions in such a way that human collaborators can easily use them to correctly anticipate what will follow. While previous work has focused on finding initial plans that convey a set goal, here we focus on finding two portions of a plan such that the initial portion conveys the final one.We introduce t-predictability: a measure that quantifies the accuracy and confidence with which human observers can predict the remaining robot plan from the overall task goal and the observed initial t actions in the plan. We contribute a method for generating t-predictable plans: we search for a full plan that accomplishes the task, but in which the first t actions make it as easy as possible to infer the remaining ones. The result is often different from the most efficient plan, in which the initial actions might leave a lot of ambiguity as to how the task will be completed. Through an online experiment and an in-person user study with physical robots, we find that our approach outperforms a traditional efficiency-based planner in objective and subjective collaboration metrics.},
	language = {en},
	urldate = {2021-02-01},
	booktitle = {Algorithmic {Foundations} of {Robotics} {XII}: {Proceedings} of the {Twelfth} {Workshop} on the {Algorithmic} {Foundations} of {Robotics}},
	author = {Fisac, Jaime F. and Liu, Chang and Hamrick, Jessica B. and Sastry, Shankar and Hedrick, J. Karl and Griffiths, Thomas L. and Dragan, Anca D.},
	year = {2020},
	pages = {144--159},
}

@inproceedings{draganGeneratingLegibleMotion2013,
	title = {Generating Legible Motion},
	booktitle = {Proceedings of {Robotics}: {Science} and {Systems}},
	author = {Dragan, Anca and Srinivasa, Siddhartha},
	year = {2013},
}

@inproceedings{kwonExpressingRobotIncapability2018,
	title = {Expressing Robot Incapability},
	abstract = {Our goal is to enable robots to express their incapability, and to do so in a way that communicates both what they are trying to accomplish and why they are unable to accomplish it. We frame this as a trajectory optimization problem: maximize the similarity between the motion expressing incapability and what would amount to successful task execution, while obeying the physical limits of the robot. We introduce and evaluate candidate similarity measures, and show that one in particular generalizes to a range of tasks, while producing expressive motions that are tailored to each task. Our user study supports that our approach automatically generates motions expressing incapability that communicate both what and why to end-users, and improve their overall perception of the robot and willingness to collaborate with it in the future.},
	urldate = {2019-09-03},
	booktitle = {Proceedings of the 2018 {ACM}/{IEEE} {International} {Conference} on {Human}-{Robot} {Interaction}},
	author = {Kwon, Minae and Huang, Sandy H. and Dragan, Anca D.},
	year = {2018},
	keywords = {Computer Science - Robotics},
	pages = {87--95},
	annote = {Comment: HRI 2018},
	file = {arXiv\:1810.08167 PDF:C\:\\Users\\mana_\\Zotero\\storage\\MM6UQZ7V\\Kwon et al. - 2018 - Expressing Robot Incapability.pdf:application/pdf;arXiv.org Snapshot:C\:\\Users\\mana_\\Zotero\\storage\\25VNTTVJ\\1810.html:text/html},
}

@article{draganDeceptiveRobotMotion2015,
	title = {Deceptive Robot Motion: Synthesis, Analysis and Experiments},
	volume = {39},
	shorttitle = {Deceptive {Robot} {Motion}},
	number = {3},
	urldate = {2019-05-10},
	journal = {Auton. Robots},
	author = {Dragan, Anca and Holladay, Rachel and Srinivasa, Siddhartha},
	year = {2015},
	keywords = {Motion planning, Human robot interaction, Legibility, Deception},
	pages = {331--345},
}


@inproceedings{mastersDeceptivePathPlanning2017,
	title = {Deceptive Path-Planning},
	language = {en},
	urldate = {2019-05-06},
	booktitle = {Proceedings of the {Twenty}-{Sixth} {International} {Joint} {Conference} on {Artificial} {Intelligence}},
	author = {Masters, Peta and Sardina, Sebastian},
	year = {2017},
	pages = {4368--4375},
}


@inproceedings{strouseLearningShareHide2018,
	title = {Learning to share and hide intentions using information regularization},
	urldate = {2020-12-09},
	booktitle = {Proceedings of the {Thirty}-{Secondnd} {International} {Conference} on {Neural} {Information} {Processing} {Systems}},
	author = {Strouse, D J and Kleiman-Weiner, Max and Tenenbaum, Josh and Botvinick, Matt and Schwab, David},
	year = {2018},
	pages = {10270--10281},
}


@inproceedings{chakrabortiBalancingExplicabilityExplanations2019,
	title = {Balancing Explicability and Explanations in Human-Aware Planning},
	abstract = {Electronic proceedings of IJCAI 2019},
	urldate = {2021-02-15},
	booktitle = {Proceedings of the {Twenty}-{Eighth} {International} {Joint} {Conference} on {Artificial} {Intelligence}},
	author = {Chakraborti, Tathagata and Sreedharan, Sarath and Kambhampati, Subbarao},
	year = {2019},
	pages = {1335--1343},
	file = {Snapshot:C\:\\Users\\mana_\\Zotero\\storage\\3HE4F8XI\\185.html:text/html},
}


@inproceedings{kulkarniUnifiedFrameworkPlanning2019,
	title = {A Unified Framework for Planning in Adversarial and Cooperative Environments},
	booktitle = {Proceedings of the {Thirty}-{Third} {AAAI} {Conference} on {Artificial} {Intelligence}},
	author = {Kulkarni, Anagha and Srivastava, Siddharth and Kambhampati, Subbarao},
	year = {2019},
	keywords = {Computer Science - Artificial Intelligence},
	pages = {2479--2487},
}


@article{bertsekasAnalysisStochasticShortest1991,
	title = {An Analysis of Stochastic Shortest Path Problems},
	volume = {16},
	abstract = {We consider a stochastic version of the classical shortest path problem whereby for each node of a graph, we must choose a probability distribution over the set of successor nodes so as to reach a certain destination node with minimum expected cost. The costs of transition between successive nodes can be positive as well as negative. We prove natural generalizations of the standard results for the deterministic shortest path problem, and we extend the corresponding theory for undiscounted finite state Markovian decision problems by removing the usual restriction that costs are either all nonnegative or all nonpositive.},
	number = {3},
	urldate = {2022-01-24},
	journal = {Mathematics of Operations Research},
	author = {Bertsekas, Dimitri P. and Tsitsiklis, John N.},
	year = {1991},
	pages = {580--595},
	file = {JSTOR Full Text PDF:C\:\\Users\\mana_\\Zotero\\storage\\AXDI257I\\Bertsekas and Tsitsiklis - 1991 - An Analysis of Stochastic Shortest Path Problems.pdf:application/pdf},
}


@article{bakerActionUnderstandingInverse2009,
	title = {Action understanding as inverse planning},
	volume = {113},
	abstract = {Humans are adept at inferring the mental states underlying other agents' actions, such as goals, beliefs, desires, emotions and other thoughts. We propose a computational framework based on Bayesian inverse planning for modeling human action understanding. The framework represents an intuitive theory of intentional agents' behavior based on the principle of rationality: the expectation that agents will plan approximately rationally to achieve their goals, given their beliefs about the world. The mental states that caused an agent's behavior are inferred by inverting this model of rational planning using Bayesian inference, integrating the likelihood of the observed actions with the prior over mental states. This approach formalizes in precise probabilistic terms the essence of previous qualitative approaches to action understanding based on an "intentional stance" [Dennett, D. C. (1987). The intentional stance. Cambridge, MA: MIT Press] or a "teleological stance" [Gergely, G., Nádasdy, Z., Csibra, G., \& Biró, S. (1995). Taking the intentional stance at 12 months of age. Cognition, 56, 165-193]. In three psychophysical experiments using animated stimuli of agents moving in simple mazes, we assess how well different inverse planning models based on different goal priors can predict human goal inferences. The results provide quantitative evidence for an approximately rational inference mechanism in human goal inference within our simplified stimulus paradigm, and for the flexible nature of goal representations that human observers can adopt. We discuss the implications of our experimental results for human action understanding in real-world contexts, and suggest how our framework might be extended to capture other kinds of mental state inferences, such as inferences about beliefs, or inferring whether an entity is an intentional agent.},
	journal = {Cognition},
	author = {Baker, Chris L. and Saxe, Rebecca and Tenenbaum, Joshua B.},
	year = {2009},
	keywords = {Inference, Thinking, function},
	pages = {329--349},
}



@inproceedings{kocsisBanditBasedMonteCarlo2006,
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Bandit Based {Monte}-{Carlo} Planning},
	language = {en},
	booktitle = {European {Conference} on {Machine} {Learning}},
	author = {Kocsis, Levente and Szepesvári, Csaba},
	year = {2006},
	pages = {282--293},
}


@book{bertsekasNeuroDynamicProgramming1996,
	title = {Neuro-Dynamic Programming},
	volume = {27},
	abstract = {From the Publisher:
This is the first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology, which is a recent breakthrough in the practical application of neural networks and dynamic programming to complex problems of planning, optimal decision making, and intelligent control.},
	author = {Bertsekas, Dimitri and Tsitsiklis, John},
	year = {1996},
    publisher = {Athena Scientific},
	doi = {10.1007/978-0-387-74759-0_440},
}


@article{bartoLearningActUsing1995a,
	title = {Learning to act using real-time dynamic programming},
	volume = {72},
	abstract = {Learning methods based on dynamic programming (DP) are receiving increasing attention in artificial intelligence. Researchers have argued that DP provides the appropriate basis for compiling planning results into reactive strategies for real-time control, as well as for learning such strategies when the system being controlled is incompletely known. We introduce an algorithm based on DP, which we call Real-Time DP (RTDP), by which an embedded system can improve its performance with experience. RTDP generalizes Korf's Learning-Real-Time-A* algorithm to problems involving uncertainty. We invoke results from the theory of asynchronous DP to prove that RTDP achieves optimal behavior in several different classes of problems. We also use the theory of asynchronous DP to illuminate aspects of other DP-based reinforcement learning methods such as Watkins' Q-Learning algorithm. A secondary aim of this article is to provide a bridge between AI research on real-time planning and learning and relevant concepts and algorithms from control theory.},
	number = {1},
	urldate = {2024-01-14},
	journal = {Artificial Intelligence},
	author = {Barto, Andrew G. and Bradtke, Steven J. and Singh, Satinder P.},
	year = {1995},
	pages = {81--138},
}

@article{kaelblingPlanningActingPartially1998,
	title = {Planning and Acting in Partially Observable Stochastic Domains},
	volume = {101},
	abstract = {In this paper, we bring techniques from operations research to bear on the problem of choosing optimal actions in partially observable stochastic domains. We begin by introducing the theory of Markov decision processes (mdps) and partially observable MDPs (pomdps). We then outline a novel algorithm for solving pomdps off line and show how, in some cases, a finite-memory controller can be extracted from the solution to a POMDP. We conclude with a discussion of how our approach relates to previous work, the complexity of finding exact solutions to pomdps, and of some possibilities for finding approximate solutions.},
	number = {1-2},
	urldate = {2019-11-09},
	journal = {Artificial Intelligence},
	author = {Kaelbling, Leslie Pack and Littman, Michael L. and Cassandra, Anthony R.},
	year = {1998},
	keywords = {Planning, Uncertainty, Partially observable Markov decision processes},
	pages = {99--134},
	file = {Submitted Version:C\:\\Users\\mana_\\Zotero\\storage\\IPJ3MW26\\Kaelbling et al. - 1998 - Planning and Acting in Partially Observable Stocha.pdf:application/pdf},
}

@inproceedings{pineauPointbasedValueIteration2003a,
	title = {Point-based {Value} {Iteration}: {An} {Anytime} {Algorithm} for {POMDPs}},
	shorttitle = {Point-based {Value} {Iteration}},
	urldate = {2019-11-11},
	booktitle = {Proceedings of the {Eighteenth} {International} {Joint} {Conference} on {Artificial} {Intelligence}},
	author = {Pineau, Joelle and Gordon, Geoff and Thrun, Sebastian},
	year = {2003},
	pages = {1025--1030},
}


@article{monahanSurveyPartiallyObservable1982,
	title = {A Survey of Partially Observable {Markov} Decision Processes: Theory, Models, and Algorithms},
	volume = {28},
	shorttitle = {A {Survey} of {Partially} {Observable} {Markov} {Decision} {Processes}},
	abstract = {[This paper surveys models and algorithms dealing with partially observable Markov decision processes. A partially observable Markov decision process (POMDP) is a generalization of a Markov decision process which permits uncertainty regarding the state of a Markov process and allows for state information acquisition. A general framework for finite state and action POMDP's is presented. Next, there is a brief discussion of the development of POMDP's and their relationship with other decision processes. A wide range of models in such areas as quality control, machine maintenance, internal auditing, learning, and optimal stopping are discussed within the POMDP-framework. Lastly, algorithms for computing optimal solutions to POMDP's are presented.]},
	number = {1},
	urldate = {2019-02-04},
	journal = {Management Science},
	author = {Monahan, George E.},
	year = {1982},
	pages = {1--16},
	file = {Monahan - 1982 - A Survey of Partially Observable Markov Decision P.pdf:C\:\\Users\\mana_\\Zotero\\storage\\F5VXIR4Z\\Monahan - 1982 - A Survey of Partially Observable Markov Decision P.pdf:application/pdf},
}

@inproceedings{zhangPlanExplicabilityPredictability2017,
    title = {Plan Explicability and Predictability for Robot Task Planning},
	booktitle = {International {Conference} on {Robotics} and {Automation}},
	author = {Zhang, Yu and Sreedharan, Sarath and Kulkarni, Anagha and Chakraborti, Tathagata and Zhuo, Hankz Hankui and Kambhampati, Subbarao},
	year = {2017},
	pages = {1313--1320},
}


@inproceedings{bacchusRewardingBehaviors1996,
	title = {Rewarding behaviors},
	volume = {2},
	abstract = {Markov decision processes (MDPs) are a very popular tool for decision theoretic planning (DTP), partly because of the welldeveloped, expressive theory that includes effective solution techniques. But the Markov assumption--that dynamics and rewards depend on the current state only, and not on history-- is often inappropriate. This is especially true of rewards: we frequently wish to associate rewards with behaviors that extend over time. Of course, such reward processes can be encoded in an MDP should we have a rich enough state space (where states encode enough history). However it is often difficult to "hand craft" suitable state spaces that encode an appropriate amount of history. We consider this problem in the case where non-Markovian rewards are encoded by assigning values to formulas of a temporal logic. These formulas characterize the value of temporally extended behaviors. We argue that this allows a natural representation of many commonly encountered non-Markovian rewards. The main result is an algorithm which, given a decision process with non-Markovian rewards expressed in this manner, automatically constructs an equivalent MDP (with Markovian reward structure), allowing optimal policy construction using standard techniques.},
	urldate = {2020-12-11},
	booktitle = {Proceedings of the {Thirteenth} {National} {Conference} on {Artificial} {Intelligence}},
	author = {Bacchus, Fahiem and Boutilier, Craig and Grove, Adam},
	year = {1996},
	pages = {1160--1167},
}


@article{thiebauxDecisionTheoreticPlanningNonMarkovian2006,
	title = {Decision-Theoretic Planning with non-{Markovian} Rewards},
	volume = {25},
	copyright = {Copyright (c)},
	language = {en},
	urldate = {2020-12-10},
	journal = {Journal of Artificial Intelligence Research},
	author = {Thiébaux, Sylvie and Gretton, Charles and Slaney, John and Price, David and Kabanza, F.},
	year = {2006},
	pages = {17--74},
	file = {Full Text PDF:C\:\\Users\\mana_\\Zotero\\storage\\2EYHQ6YQ\\Thiebaux et al. - 2006 - Decision-Theoretic Planning with non-Markovian Rew.pdf:application/pdf;Snapshot:C\:\\Users\\mana_\\Zotero\\storage\\SHJWDR7K\\10438.html:text/html},
}

@inproceedings{brafmanLTLfLDLfNonMarkovian2018,
	title = {{LTLf}/{LDLf} Non-{Markovian} Rewards},
	abstract = {In Markov Decision Processes (MDPs), the reward obtained in a state is Markovian, i.e., depends on the last state and action. This dependency makes it difficult to reward more interesting long-term behaviors, such as always closing a door after it has been opened, or providing coffee only following a request. Extending MDPs to handle non-Markovian reward functions was the subject of two previous lines of work. Both use LTL variants to specify the reward function and then compile the new model back into a Markovian model. Building on recent progress in temporal logics over finite traces, we adopt LDLf for specifying non-Markovian rewards and provide an elegant automata construction for building a Markovian model, which extends that of previous work and offers strong minimality and compositionality guarantees.},
	booktitle = {Proceedings of the {Thirty}-{Second} {AAAI} {Conference} on {Artificial} {Intelligence}},
	author = {Brafman, R. and Giacomo, Giuseppe De and Patrizi, F.},
	year = {2018},
	pages = {1771--1778},
}


@article{gmytrasiewiczFrameworkSequentialPlanning2005,
	title = {A Framework for Sequential Planning in Multi-Agent Settings},
	volume = {24},
	language = {en},
	urldate = {2020-12-07},
	journal = {Journal of Artificial Intelligence Research},
	author = {Gmytrasiewicz, Piotr J. and Prashant, Doshi},
	year = {2005},
	pages = {49--79},
}


@article{battigalliBeliefDependentMotivationsPsychological2022b,
	title = {Belief-Dependent Motivations and Psychological Game Theory},
	volume = {60},
	language = {en},
	number = {3},
	urldate = {2024-01-16},
	journal = {Journal of Economic Literature},
	author = {Battigalli, Pierpaolo and Dufwenberg, Martin},
	year = {2022},
	pages = {833--882},
}

@book{pereaEpistemicGameTheory2012,
	title = {Epistemic Game Theory: Reasoning and Choice},
	shorttitle = {Epistemic {Game} {Theory}},
	language = {en},
	publisher = {Cambridge University Press},
	author = {Perea, Andrés},
	year = {2012},
	keywords = {Business \& Economics / Economics / Microeconomics, Mathematics / Game Theory},
}

@article{chowOptimalOnewayMultigrid1991,
	title = {An optimal one-way multigrid algorithm for discrete-time stochastic control},
	volume = {36},
	abstract = {The numerical solution of discrete-time stationary infinite-horizon discounted stochastic control problems is considered for the case where the state space is continuous and the problem is to be solved approximately, within a desired accuracy. After a discussion of problem discretization, the authors introduce a multigrid version of the successive approximation algorithm that proceeds 'one way' from coarse to fine grids, and analyze its computational requirements as a function of the desired accuracy and of the discount factor. They also study the effects of a certain mixing (ergodicity) condition on the algorithm's performance. It is shown that the one-way multigrid algorithm improves upon the complexity of its single-grid variant and is, in a certain sense, optimal.{\textless}{\textgreater}},
	number = {8},
	urldate = {2024-01-17},
	journal = {IEEE Transactions on Automatic Control},
	author = {Chow, C.-S. and Tsitsiklis, J.N.},
	year = {1991},
    pages = {898--914},
}


@article{munosVariableResolutionDiscretization2002,
	title = {Variable Resolution Discretization in Optimal Control},
	volume = {49},
	abstract = {The problem of state abstraction is of central importance in optimal control, reinforcement learning and Markov decision processes. This paper studies the case of variable resolution state abstraction for continuous time and space, deterministic dynamic control problems in which near-optimal policies are required. We begin by defining a class of variable resolution policy and value function representations based on Kuhn triangulations embedded in a kd-trie. We then consider top-down approaches to choosing which cells to split in order to generate improved policies. The core of this paper is the introduction and evaluation of a wide variety of possible splitting criteria. We begin with local approaches based on value function and policy properties that use only features of individual cells in making split choices. Later, by introducing two new non-local measures, influence and variance, we derive splitting criteria that allow one cell to efficiently take into account its impact on other cells when deciding whether to split. Influence is an efficiently-calculable measure of the extent to which changes in some state effect the value function of some other states. Variance is an efficiently-calculable measure of how risky is some state in a Markov chain: a low variance state is one in which we would be very surprised if, during any one execution, the long-term reward attained from that state differed substantially from its expected value, given by the value function.},
	language = {en},
	number = {2},
	urldate = {2023-12-04},
	journal = {Machine Learning},
	author = {Munos, Rémi and Moore, Andrew},
	year = {2002},
	keywords = {reinforcement learning, optimal control, adaptive mesh refinement, variable resolution discretization},
	pages = {291--323},
}

@inproceedings{rachelsonLocalityActionDomination2010a,
	title = {On the locality of action domination in sequential decision making},
	abstract = {In the field of sequential decision making and reinforcement learning, it has been observed that good policies for most problems exhibit a significant amount of structure. In practice, this implies that when a learning agent discovers an action is better than any other in a given state, this action actually happens to also dominate in a certain neighbourhood around that state. This paper presents new results proving that this notion of locality in action domination can be linked to the smoothness of the environment’s underlying stochastic model. Namely, we link the Lipschitz continuity of a Markov Decision Process to the Lispchitz continuity of its policies’ value functions and introduce the key concept of influence radius to describe the neighbourhood of states where the dominating action is guaranteed to be constant. These ideas are directly exploited into the proposed Localized Policy Iteration (LPI) algorithm, which is an active learning version of Rollout-based Policy Iteration. Preliminary results on the Inverted Pendulum domain demonstrate the viability and the potential of the proposed approach.},
	urldate = {2024-02-02},
	author = {Rachelson, E. and Lagoudakis, M.},
	year = {2010},
	annote = {[TLDR] Results are presented proving that this notion of locality in action domination can be linked to the smoothness of the environment’s underlying stochastic model and introducing the key concept of influence radius to describe the neighbourhood of states where the dominating action is guaranteed to be constant.},
}

@incollection{gordonStableFunctionApproximation1995,
	title = {Stable Function Approximation in Dynamic Programming},
	abstract = {The success of reinforcement learning in practical problems depends on the ability to combine function approximation with temporal difference methods such as value iteration. Experiments in this area have produced mixed results; there have been both notable successes and notable disappointments. Theory has been scarce, mostly due to the difficulty of reasoning about function approximators that generalize beyond the observed data. We provide a proof of convergence for a wide class of temporal difference methods involving function approximators such as k-nearest-neighbor, and show experimentally that these methods can be useful. The proof is based on a view of function approximators as expansion or contraction mappings. In addition, we present a novel view of fitted value iteration: an approximate algorithm for one environment turns out to be an exact algorithm for a different environment.},
	urldate = {2024-02-02},
	booktitle = {Machine {Learning} {Proceedings} 1995},
	publisher = {Morgan Kaufmann},
	author = {Gordon, Geoffrey J.},
	year = {1995},
	pages = {261--268},
}

@article{freudenthalSimplizialzerlegungenBeschrankterFlachheit1942,
	title = {Simplizialzerlegungen von {Beschrankter} {Flachheit}},
	volume = {43},
	number = {3},
	urldate = {2024-02-06},
	journal = {Annals of Mathematics},
	author = {Freudenthal, Hans},
	year = {1942},
	note = {Publisher: Annals of Mathematics},
	pages = {580--582},
	file = {JSTOR Full Text PDF:C\:\\Users\\mana_\\Zotero\\storage\\3Q3ET8DS\\Freudenthal - 1942 - Simplizialzerlegungen von Beschrankter Flachheit.pdf:application/pdf},
}

@article{savasDeceptiveDecisionmakingUncertainty2022,
  title = {Deceptive Decision-Making under Uncertainty},
  author = {Savas, Yagiz and Verginis, Christos K. and Topcu, Ufuk},
  year = {2022},
  journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume = {36},
  number = {5},
  pages = {5332--5340},
  urldate = {2024-02-20},
  abstract = {We study the design of autonomous agents that are capable of deceiving outside observers about their intentions while carrying out tasks in stochastic, complex environments. By modeling the agent's behavior as a Markov decision process, we consider a setting where the agent aims to reach one of multiple potential goals while deceiving outside observers about its true goal. We propose a novel approach to model observer predictions based on the principle of maximum entropy and to efficiently generate deceptive strategies via linear programming. The proposed approach enables the agent to exhibit a variety of tunable deceptive behaviors while ensuring the satisfaction of probabilistic constraints on the behavior. We evaluate the performance of the proposed approach via comparative user studies and present a case study on the streets of Manhattan, New York, using real travel time distributions.},
  file = {C:\Users\mana_\Zotero\storage\P378AIVA\Savas et al. - 2022 - Deceptive Decision-Making under Uncertainty.pdf}
}

@inproceedings{miuraObserverawarePlanningImplicit2024,
  title = {Observer-Aware Planning with Implicit and Explicit Communication},
  booktitle = {Proceedings of the 23rd {{International Conference}} on {{Autonomous Agents}} and {{Multiagent Systems}}},
  author = {Miura, Shuwa and Zilberstein, Shlomo},
  year = {2024},
  pages = {1409--1417},
  urldate = {2024-05-12},
  abstract = {This paper presents a computational model designed for planning both implicit and explicit communication of intentions, goals, and desires. Building upon previous research focused on implicit communication of intention via actions, our model seeks to strategically influence an observer's belief using both the agent's actions and explicit messages. We show that our proposed model can be considered to be a special case of general multi-agent problems with explicit communication under certain assumptions. Since the mental state of the observer depends on histories, computing a policy for the proposed model amounts to optimizing a non-Markovian objective, which we show to be intractable in the worst case. To mitigate this challenge, we propose a technique based on splitting domain and communication actions during planning. We conclude with experimental evaluations of the proposed approach that illustrate its effectiveness},
  copyright = {All rights reserved},
  keywords = {hri,legibility,mdp,theory of mind}
}


@book{dennettIntentionalStance1987,
  title = {The Intentional Stance},
  author = {Dennett, Daniel C.},
  year = {1987},
  pages = {xi, 388},
  publisher = {The MIT Press},
  abstract = {The theory of intentionality presented in this book has been gradually evolving over about twenty years. While the central ideas found rudimentary expression in "Content and Consciousness" in 1969, it was the publication 1971 of "Intentional Systems" that initiated the series of articles about what I call the intentional stance and the objects one discovers from that stance: intentional systems. The first three of these articles (Dennett 1971, 1973, 1976b) were reprinted in "Brainstorms" in 1978, and critics and students often treat that book as the canonical, target expression of my view. I soon found, however, that the defense of my position was evolving further in response to criticism, and so I was driven to compose a series of post-"Brainstorms" essays in which I attempted to revise, re-express, and extend my view. (PsycINFO Database Record (c) 2016 APA, all rights reserved)},
  keywords = {Cognitive Processes,Intention},
  file = {C:\Users\mana_\Zotero\storage\VH4ESVEC\1987-98612-000.html}
}

@inproceedings{ziebartMaximumEntropyInverse2008,
  title = {Maximum Entropy Inverse Reinforcement Learning},
  booktitle = {Proceedings of the 23rd {{National Conference}} on {{Artificial Intelligence}} - {{Volume}} 3},
  author = {Ziebart, Brian D. and Maas, Andrew and Bagnell, J. Andrew and Dey, Anind K.},
  year = {2008},
  pages = {1433--1438},
  urldate = {2019-08-28},
  abstract = {Recent research has shown the benefit of framing problems of imitation learning as solutions to Markov Decision Problems. This approach reduces learning to the problem of recovering a utility function that makes the behavior induced by a near-optimal policy closely mimic demonstrated behavior. In this work, we develop a probabilistic approach based on the principle of maximum entropy. Our approach provides a well-defined, globally normalized distribution over decision sequences, while providing the same performance guarantees as existing methods. We develop our technique in the context of modeling real-world navigation and driving behaviors where collected data is inherently noisy and imperfect. Our probabilistic approach enables modeling of route preferences as well as a powerful new approach to inferring destinations and routes based on partial trajectories.},
  file = {C:\Users\mana_\Zotero\storage\ZXJSGULL\Ziebart et al. - Maximum Entropy Inverse Reinforcement Learning.pdf}
}

@inproceedings{zhi-xuanOnlineBayesianGoal2020,
  title = {Online {Bayesian} Goal Inference for Boundedly Rational Planning Agents},
  author = {{Zhi-Xuan}, Tan and Mann, Jordyn and Silver, Tom and Tenenbaum, Josh and Mansinghka, Vikash},
  year = {2020},
  booktitle = {Advances in Neural Information Processing Systems},
  volume = {33},
  pages = {19238--19250},
  urldate = {2021-04-05},
  langid = {english},
  file = {C\:\\Users\\mana_\\Zotero\\storage\\QF3ESZXD\\Zhi-Xuan et al. - 2020 - Online Bayesian Goal Inference for Boundedly Ratio.pdf;C\:\\Users\\mana_\\Zotero\\storage\\QTS8U4X7\\df3aebc649f9e3b674eeb790a4da224e-Abstract.html}
}

@article{lepersHowExhibitMore2024a,
  title = {How to Exhibit More Predictable Behaviors},
  author = {Lepers, Salom{\'e} and Lemonnier, Sophie and Thomas, Vincent and Buffet, Olivier},
  year = {2024},
  number = {arXiv:2404.11296},
  eprint = {2404.11296},
  primaryclass = {cs},
  publisher = {arXiv},
  urldate = {2024-05-27},
  abstract = {This paper looks at predictability problems, i.e., wherein an agent must choose its strategy in order to optimize the predictions that an external observer could make. We address these problems while taking into account uncertainties on the environment dynamics and on the observed agent's policy. To that end, we assume that the observer 1. seeks to predict the agent's future action or state at each time step, and 2. models the agent using a stochastic policy computed from a known underlying problem, and we leverage on the framework of observer-aware Markov decision processes (OAMDPs). We propose action and state predictability performance criteria through reward functions built on the observer's belief about the agent policy; show that these induced predictable OAMDPs can be represented by goal-oriented or discounted MDPs; and analyze the properties of the proposed reward functions both theoretically and empirically on two types of grid-world problems.},
  archiveprefix = {arxiv},
  keywords = {Computer Science - Artificial Intelligence},
  file = {C\:\\Users\\mana_\\Zotero\\storage\\5DNBQ33Y\\Lepers et al. - 2024 - How to Exhibit More Predictable Behaviors.pdf;C\:\\Users\\mana_\\Zotero\\storage\\PVYBS82X\\2404.html}
}

@article{harsanyiGamesIncompleteInformation1968,
  title = {Games with Incomplete Information Played by ``{Bayesian}'' Players, {I-III}. {Part II}. {{Bayesian Equilibrium Points}}},
  author = {Harsanyi, John C.},
  year = {1968},
  journal = {Management Science},
  volume = {14},
  number = {5},
  pages = {320--334},
  urldate = {2024-03-14},
  abstract = {Part I of this paper has described a new theory for the analysis of games with incomplete information. It has been shown that, if the various players' subjective probability distributions satisfy a certain mutual-consistency requirement, then any given game with incomplete information will be equivalent to a certain game with complete information, called the "Bayes-equivalent" of the original game, or briefly a "Bayesian game." Part II of the paper will now show that any Nash equilibrium point of this Bayesian game yields a "Bayesian equilibrium point" for the original game and conversely. This result will then be illustrated by numerical examples, representing two-person zero-sum games with incomplete information. We shall also show how our theory enables us to analyze the problem of exploiting the opponent's erroneous beliefs. However, apart from its indubitable usefulness in locating Bayesian equilibrium points, we shall show it on a numerical example (the Bayes-equivalent of a two-person cooperative game) that the normal form of a Bayesian game is in many cases a highly unsatisfactory representation of the game situation and has to be replaced by other representations (e.g., by the semi-normal form). We shall argue that this rather unexpected result is due to the fact that Bayesian games must be interpreted as games with "delayed commitment" whereas the normal-form representation always envisages a game with "immediate commitment."},
  file = {C:\Users\mana_\Zotero\storage\VDZE5EJK\Harsanyi - 1968 - Games with Incomplete Information Played by Bayes.pdf}
}

@article{alonDisInformationTheory2023,
  title = {A (Dis-)Information Theory of Revealed and Unrevealed Preferences: Emerging Deception and Skepticism via Theory of Mind},
  shorttitle = {A ({{Dis-}})Information {{Theory}} of {{Revealed}} and {{Unrevealed Preferences}}},
  author = {Alon, Nitay and Schulz, Lion and Rosenschein, Jeffrey S. and Dayan, Peter},
  year = {2023},
  journal = {Open Mind},
  volume = {7},
  pages = {608--624},
  urldate = {2024-02-09},
  abstract = {In complex situations involving communication, agents might attempt to mask their intentions, exploiting Shannon's theory of information as a theory of misinformation. Here, we introduce and analyze a simple multiagent reinforcement learning task where a buyer sends signals to a seller via its actions, and in which both agents are endowed with a recursive theory of mind. We show that this theory of mind, coupled with pure reward-maximization, gives rise to agents that selectively distort messages and become skeptical towards one another. Using information theory to analyze these interactions, we show how savvy buyers reduce mutual information between their preferences and actions, and how suspicious sellers learn to reinterpret or discard buyers' signals in a strategic manner.},
  langid = {english},
  file = {C:\Users\mana_\Zotero\storage\YTHRUTQA\Alon et al. - 2023 - A (Dis-)information Theory of Revealed and Unrevea.pdf}
}

@inproceedings{loPlanningPartnerUncertainty2020,
  title = {Planning with Partner Uncertainty Modeling for Efficient Information Revealing in Teamwork},
  booktitle = {Proceedings of the 2020 {{ACM}}/{{IEEE International Conference}} on {{Human-Robot Interaction}}},
  author = {Lo, Shih-Yun and Short, Elaine Schaertl and Thomaz, Andrea L.},
  year = {2020},
  pages = {319--327},
  urldate = {2020-11-24},
  abstract = {Communication among team members is important for efficient teamwork, to coordinate behavior and ensure that all team members have the information they need to complete the task. To enable effective communication and thus efficient teamwork, we propose a multi-agent planning approach to revealing information based on its benefit to joint team performance. By explicitly modeling the partner's knowledge and behavior, our approach allows a robot in a team to reason about when information is useful, how the communication is effective, and to communicate through efficient actions. That is, the robot provides only the necessary information for task completion, provides the information at the time that it is needed, and through the action(s) that optimizes team performance. We validated this approach in a human study in which participants walk together with a robot to a destination that is known only to the robot. We compared to a legible motion generation approach, and showed that users perceived our approach as more natural, socially appropriate, and fluent to team with, while being both more predictable and intent-clear. The ratings of our approach are equal or higher than legible motion across all 18 survey items.},
  keywords = {human-robot interaction,multi-agent planning,social navigation}
}

@article{seukenFormalModelsAlgorithms2008,
  title = {Formal Models and Algorithms for Decentralized Decision Making under Uncertainty},
  author = {Seuken, Sven and Zilberstein, Shlomo},
  year = {2008},
  journal = {Autonomous Agents and Multi-Agent Systems},
  volume = {17},
  number = {2},
  pages = {190--250},
  urldate = {2024-04-10},
  abstract = {Over the last 5 years, the AI community has shown considerable interest in decentralized control of multiple decision makers or ``agents'' under uncertainty. This problem arises in many application domains, such as multi-robot coordination, manufacturing, information gathering, and load balancing. Such problems must be treated as decentralized decision problems because each agent may have different partial information about the other agents and about the state of the world. It has been shown that these problems are significantly harder than their centralized counterparts, requiring new formal models and algorithms to be developed. Rapid progress in recent years has produced a number of different frameworks, complexity results, and planning algorithms. The objectives of this paper are to provide a comprehensive overview of these results, to compare and contrast the existing frameworks, and to provide a deeper understanding of their relationships with one another, their strengths, and their weaknesses. While we focus on cooperative systems, we do point out important connections with game-theoretic approaches. We analyze five different formal frameworks, three different optimal algorithms, as well as a series of approximation techniques. The paper provides interesting insights into the structure of decentralized problems, the expressiveness of the various models, and the relative advantages and limitations of the different solution techniques. A better understanding of these issues will facilitate further progress in the field and help resolve several open problems that we identify.},
  copyright = {http://www.springer.com/tdm},
  langid = {english},
  file = {C:\Users\mana_\Zotero\storage\SMC3NJRF\Seuken and Zilberstein - 2008 - Formal models and algorithms for decentralized dec.pdf}
}

@article{gongExplicablePolicySearch2022,
  title = {Explicable Policy Search},
  author = {Gong, Ze and Zhang, Yu},
  year = {2022},
  journal = {Advances in Neural Information Processing Systems},
  volume = {35},
  pages = {38859--38872},
  urldate = {2024-03-15},
  langid = {english},
  file = {C:\Users\mana_\Zotero\storage\NXM26ME7\Gong et al. - 2022 - Explicable Policy Search.pdf}
}

@article{thellmanFolkPsychologicalInterpretationHuman2017,
  title = {Folk-Psychological Interpretation of Human vs. Humanoid Robot Behavior: Exploring the Intentional Stance toward Robots},
  shorttitle = {Folk-{{Psychological Interpretation}} of {{Human}} vs. {{Humanoid Robot Behavior}}},
  author = {Thellman, Sam and Silvervarg, Annika and Ziemke, Tom},
  year = {2017},
  journal = {Frontiers in Psychology},
  volume = {8},
  publisher = {Frontiers},
  urldate = {2024-03-08},
  abstract = {{$<$}p{$>$}People rely on shared folk-psychological theories when judging behavior. These theories guide people's social interactions and therefore need to be taken into consideration in the design of robots and other autonomous systems expected to interact socially with people. It is, however, not yet clear to what degree the mechanisms that underlie people's judgments of robot behavior overlap or differ from the case of human or animal behavior. To explore this issue, participants ({$<$}italic{$>$}N{$<$}/italic{$>$} = 90) were exposed to images and verbal descriptions of eight different behaviors exhibited either by a person or a humanoid robot. Participants were asked to rate the intentionality, controllability and desirability of the behaviors, and to judge the plausibility of seven different types of explanations derived from a recently proposed psychological model of lay causal explanation of human behavior. Results indicate: substantially {$<$}italic{$>$}similar judgments{$<$}/italic{$>$} of human and robot behavior, both in terms of (1a) ascriptions of intentionality/controllability/desirability and in terms of (1b) plausibility judgments of behavior explanations; (2a) {$<$}italic{$>$}high level of agreement{$<$}/italic{$>$} in judgments of robot behavior -- (2b) slightly lower but still largely similar to agreement over human behaviors; (3) {$<$}italic{$>$}systematic differences{$<$}/italic{$>$} in judgments concerning the plausibility of goals and dispositions as explanations of human vs. humanoid behavior. Taken together, these results suggest that people's intentional stance toward the robot was in this case very similar to their stance toward the human.{$<$}/p{$>$}},
  langid = {english},
  keywords = {Attribution theory,Behavior explanation,folk psychology,human-robot interaction,intentional stance,intentionality attribution,social interaction,social robot},
  file = {C:\Users\mana_\Zotero\storage\IR38VJ8L\Thellman et al. - 2017 - Folk-Psychological Interpretation of Human vs. Hum.pdf}
}

@article{perez-osorioAdoptingIntentionalStance2020,
  title = {Adopting the Intentional Stance toward Natural and Artificial Agents},
  author = {{Perez-Osorio}, Jairo and Wykowska, Agnieszka},
  year = {2020},
  journal = {Philosophical Psychology},
  volume = {33},
  number = {3},
  pages = {369--395},
  publisher = {Taylor \& Francis},
  abstract = {In our daily lives, we need to predict and understand others' behavior in order to navigate through our social environment. Predictions concerning other humans' behavior usually refer to their mental states, such as beliefs or intentions. Such a predictive strategy is called `adoption of the intentional stance.' In this paper, we review literature related to the concept of intentional stance from the perspectives of philosophy, psychology, human development, culture, and human-robot interaction. We propose that adopting the intentional stance might be a pivotal factor in facilitating social attunement with artificial agents. The paper first reviews the theoretical considerations regarding the intentional stance and examines literature related to the development of the intentional stance across life span. Subsequently, we discuss cultural norms as grounded in the intentional stance, and finally, we focus on the issue of adopting the intentional stance toward artificial agents, such as humanoid robots. At the dawn of the artificial intelligence era, the question of how -- and also when -- we predict and explain robots' behavior by referring to mental states is of high interest. The paper concludes with a discussion on ethical consequences of adopting the intentional stance toward robots, and sketches future directions in research on this topic. (PsycInfo Database Record (c) 2021 APA, all rights reserved)},
  keywords = {Human Computer Interaction,Human Development,Intention,Mentalization,Philosophies,Robotics,Social Cognition,Social Environments},
  file = {C\:\\Users\\mana_\\Zotero\\storage\\AYX5NGJC\\Perez-Osorio and Wykowska - 2020 - Adopting the intentional stance toward natural and.pdf;C\:\\Users\\mana_\\Zotero\\storage\\B5S5FUER\\2019-77443-001.html}
}
