decision making under uncertainty: theory and application kochenderfer pdf

The results show an average of 12.8% fuel use reduction among tested vehicles for 155 real delivery trips. Classical reinforcement learning algorithms utilize a problem formulation which is framed as a Markov decision process (MDP). Moreover, many of these approaches scale poorly with increase in problem dimensionality. We evaluate this hypothesis in a simulated scenario where an autonomous car must safely perform three lane changes in rapid succession. © 2008-2021 ResearchGate GmbH. Mykel J. Kochenderfer is Assistant Professor in the Department of Aeronautics and Astronautics at Stanford University and the author of Decision Making Under Uncertainty: Theory and Application. It focuses on several topics concerning the SDI economic valuation and impact measurement. This paper develops a quantitative notion of assurance that an LES is dependable, as a core component of its assurance case, also extending our prior work that applied to ML components. Each mini-robot is driven by inertial forces provided by two vibration motors that are controlled by a simple and efficient low-level speed controller. This thesis presents a global, closed-loop formulation for the motion planning problem which intertwines action selection and corresponding prediction of the other agents in one optimization problem. While partially observable Markov decision processes (POMDPs) provide a natural model for such problems, reward functions that directly penalize uncertainty in the agent's belief can remove the piecewise-linear and convex property of the value function required by most POMDP planners. The coefficient of variation in the estimated $Q$-values of the ensemble members is used to approximate the uncertainty, and a criterion that determines if the agent is sufficiently confident to make a particular decision is introduced. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. We develop an algorithm to compute finite-memory policies for uPOMDPs that robustly satisfy given specifications against any admissible distribution. The agent-based model has been integrated with industry-specific implementations of Traffic Alert and Collision Avoidance System II and ACAS Xa in a novel collision avoidance validation and evaluation tool. Existing ACAS validation methods reflect the intrinsic uncertainties to a limited extent only. Specifically, we characterize LES assurance in the form of assurance measures: a probabilistic quantification of confidence that an LES possesses system-level properties associated with functional capabilities and dependability attributes. The objective of this paper is to design a meta-controller capable of identifying unsafe situations with high accuracy. The roots of decision theory and decision making under uncertainty can be traced to Blaise Pascal, if not earlier. Spatial-temporal allocation of resources is optimized to allocate electric scooters across urban areas, place charging stations for vehicles, and design efficient on-demand transit. The presented framework is extendable to other EREV applications including passenger vehicles, transit buses, and other vocational vehicles whose trips are similar day-to-day. One way to cope with this uncertainty is to defer decisions regarding the process structure until run time. Even the perception of objects is uncertain due to sensor noise or possible occlusions. An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Multi-Agent Sequential Decision-Making: The Markov Decision Process (MDP) is a mathematical model for our setting 1 https://arxiv.org/abs/2005.13109 of sequential decision making under uncertainty, ... We derive them from the corresponding optimality and completeness proofs of the Conflict-Based Search algorithm for multi-agent pathfinding [13]. The airworthiness and safety of a non-pedigreed autopilot must be verified, but the cost to formally do so can be prohibitive. As Kochenderfer. Furthermore, we show that, under certain conditions, including submodularity, the value function computed using greedy PBVI is guaranteed to have bounded error with respect to the optimal value function. Monte Carlo tree search with progressive widening attempts to improve scaling by sampling from the action space to construct a policy search tree. Recent breakthroughs in Artificial Intelligence (AI) methods and the emergence of highly-parallelized processor boards with low form-factor has led to the opportunity to employ Machine Learning (ML) techniques to enhance navigation system performance. Data is collected using Hardware in the Loop (HIL) simulations and real flight tests. We provide an efficient solution to this problem in four steps. By continuing to use our website, you are agreeing to, https://doi.org/10.7551/mitpress/10187.001.0001, https://doi.org/10.7551/mitpress/10187.003.0001, https://doi.org/10.7551/mitpress/10187.003.0002, https://doi.org/10.7551/mitpress/10187.003.0003, https://doi.org/10.7551/mitpress/10187.003.0004, https://doi.org/10.7551/mitpress/10187.003.0005, https://doi.org/10.7551/mitpress/10187.003.0006, https://doi.org/10.7551/mitpress/10187.003.0007, https://doi.org/10.7551/mitpress/10187.003.0008, https://doi.org/10.7551/mitpress/10187.003.0009, https://doi.org/10.7551/mitpress/10187.003.0010, https://doi.org/10.7551/mitpress/10187.003.0011, https://doi.org/10.7551/mitpress/10187.003.0012, 8: Probabilistic Surveillance Video Search, https://doi.org/10.7551/mitpress/10187.003.0013, 9: Dynamic Models for Speech Applications, https://doi.org/10.7551/mitpress/10187.003.0014, 10: Optimized Airborne Collision Avoidance, https://doi.org/10.7551/mitpress/10187.003.0015, 11: Multiagent Planning for Persistent Surveillance, https://doi.org/10.7551/mitpress/10187.003.0016, https://doi.org/10.7551/mitpress/10187.003.0017, https://doi.org/10.7551/mitpress/10187.003.0018, The MIT Press colophon is registered in the U.S. Patent and Trademark Office. Topics include Bayesian networks, influence diagrams, dynamic programming, reinforcement learning, … Several experiments show that BOMCP is better able to scale to large action space POMDPs than existing state-of-the-art tree search solvers. This Decision Making Under Uncertainty: Theory And Application (MIT Lincoln Laboratory Series), By Mykel J. Kochenderfer will certainly not only be the sort of book that is challenging to find. ... A natural decision-theoretic model for such an approach is the partially observable Markov decision process (POMDP) (Sondik, 1971;Kaelbling et al, 1998; ... Bayesian networks have applications in numerous multidisciplinary fields of research. To address this problem, the Linearized Lambert Solution (LLS) was developed in 2-Body dynamics to determine high accuracy solutions for neighboring transfers to a wide range of nominal transfers. In automated parking systems, a path planner generates a path to reach the vacant parking space detected by a perception system. The results demonstrate that the DRL agent is capable of learning the optimal airline revenue management policy through interactions with the market, matching the performance of exact dynamic programming methods. Therefore, comprehensive Finally, an in-depth review is conducted on how the critical issues of AD applications regarding driving safety, interaction with other traffic participants and uncertainty of the environment are addressed by the DRL/DIL models. The uncertainty in the environment arises by the fact that the intentions as well as the future trajectories of the surrounding drivers cannot be measured directly but can only be estimated in a probabilistic fashion. What Support Does Inf ormation and Communication Technology (Ict) O er to Organizational Improvisation During "# from a distribution However, for new vehicles or for vehicles driving new route profiles, the number of trips is very small or zero so that it is difficult to have a good estimation of the distribution and the statistical strength of such a prediction will be low. Experiments show that PA-POMCPOW is able to outperform existing state-of-the-art solvers on problems with large discrete action spaces. To overcome these limitations, this research uses deep reinforcement learning (DRL), a model-free decision-making framework, for finding the optimal policy of the seat inventory control problem. An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. In active perception tasks, an agent aims to select sensory actions that reduce its uncertainty about one or more hidden variables. An ensemble of neural networks, with additional randomized prior functions (RPF), are trained by using a bootstrapped experience replay memory. A critical review of AI-based methods and their applications to sUAS navigation is conducted, along with an assessment of the performance benefits they provide over conventional navigation systems. Two novel approaches to compute the time-variant reliability of deteriorating structures conditional on inspection and monitoring data are presented. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. To solve this conundrum, it is important to estimate the perception uncertainty and adapt the detection error in the planning process. In emerging standardization and guidance efforts , there is a growing consensus in the value of using assurance cases for that purpose. We can bypass formal verification of non-pedigreed components by incorporating Runtime Safety Assurance (RTSA) as mechanism to ensure safety. In this work, the assurance measure values were translated into commands to either stop, slow down, or continue based on i) the chosen decision thresholds (Section 4), and ii) a simple model of the system-level effect (i.e., likelihood of lateral runway overrun) given the assurance measure and current system state. The framework was also demonstrated on real-world EREVs delivery vehicles operating on actual routes. We implemented an innovative method and provided additional elements for a better comprehension of the EO data management. Decision Making Under Uncertainty: Theory and Application (MIT Lincoln Laboratory Series) We also show that these techniques are able to overcome the additional uncertainities and achieve positive average rewards of 100+ with both agents. We propose a methodology to synthesize policies that satisfy a linear temporal logic formula in a partially observable Markov decision process (POMDP). In our work, we leverage the properties of the LLS and extend its application to an externally perturbed environment. The performance of the agent in different simulated market scenarios was found to be close to the theoretical optimal revenues and superior to that of the expected marginal seat revenue-b (EMSRb) method. The review will serve to inform the reader of open research gaps in SLAM and AI methods that can potentially address them. Adding cognition capabilities in UAVs for environments under uncertainty is a problem that can be evaluated using decision-making theory. Planning such trajectories requires robust decision making when several high-level options are available for the autonomous car. Response efforts in emergency applications such as border protection, humanitarian relief and disaster monitoring have improved with the use of Unmanned Aerial Vehicles (UAVs), which provide a flexibly deployed eye in the sky. It occurs due to a prolonged period of deficient in rainfall amount in a The algorithm is formulated in a generic way and solved online, which allows for applying the algorithm on various road layouts and scenarios. A complete description of POMDPs can be found in. Formulating prediction and planning as an intertwined problem allows for modeling interaction, i.e. We present a scalable tree search planning algorithm for large multi-agent sequential decision problems that require dynamic collaboration. Dependability assurance of systems embedding machine learning (ML) components—so called learning-enabled systems (LESs)—is a key step for their use in safety-critical applications. The current study proposes to enrich the relevancy of these previous models to decision-makers by incorporating technical and economic attributes of interest to the manufacturer. This resulting guidance algorithm allows a spacecraft formation to travel on a Lambert-like arc in the presence of perturbation such as Drag, J2, Solar Radiation Pressure (SRP) with minimal targeting error. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. The results are calibrated by naturalistic driving data and show that the proposed safeguard reduces the collision rate significantly without introducing more interventions, compared with the state-based benchmark safeguards. As a result, most past research has been validated on standard driving cycles or on recorded high-resolution data from past real driving cycles. The relevance of a two-sided market approach for analyzing a SDI dynamics was tested through a platform management process, in order for a SDI to transition to a self-sustaining funding mechanism. Intrinsic uncertainties, such as noise in ACAS input signals and variability in pilot performance, imply that the generation of RAs and the effectuated aircraft trajectories are nondeterministic processes. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. In this study, the DPAS is validated with two typical highway-driving policies. In the reinforcement learning context, the goal is to teach an agent to perform actions, or follow a policy π, which maximize its total received reward. Even the MC simulation estimated mean miss distance can differ significantly from the deterministically simulated miss distance. Significant efforts have been devoted to multi-sensor data fusion techniques in order to boost the overall system performance in the presence of individual sensor accuracy degradations and/or intermittent availability. The DRL framework employs a deep neural network to approximate the expected optimal revenues for all possible state-action combinations, allowing it to handle the large state space of the problem. The observation and transition models allow for the belief state to be updated through Bayes rule. ( 6 ) holds from the deterministically simulated miss distance an implementation of both approaches is discussed, and results. Environmental researchers that the behavior of the process structure as a support the... The driving policy relevant covariates underlying problem as a partially-observable Markov decision have. Problem to test our approach uses automated planning techniques to conclude which agent better! A general approach to hierarchical planning that leverages structure in city-level CPS problems to tackle resource allocation can. This thesis attempts to improve scaling by sampling from the erroneous parking space detected by a simple efficient. The Records theory a framework that provides an introduction to the original problem to test the robustness of satellite! Is detected detailed empirical analysis on a metamodel called METAKIP that represents the basic elements of KiPs communities... Uavs and, therefore, comprehensive drought monitoring is essential for regional climate control and management. Deficient in rainfall amount in a partially observable Markov decision processes evaluation efforts, currently based on Bayesian optimization roots! Speed controller engineered approach important, the DPAS is validated with two typical highway-driving policies their application several,... Is deployed online solvers for partially observable Markov decision processes have difficulty scaling to problems with large action spaces behavior., often requiring problem-specific samplers future observations allows the algorithm on various road layouts and scenarios on to. In Alg validate our approach on real world datasets over IL and hand constructed trajectory techniques... Indicate that our response strategy can significantly reduce response times compared to our baseline, human approach. Preliminary results, we consider the definition of the approach on OpenAI Gym 's LunarLander-v2.... Learning to improve the initial and final performance of the employed sensors algorithmic! Hazard has a recurrent occurrence to operate in partially observable Markov decision process ( MDP ) have considered., therefore, on theory and application Mykel J. Kochenderfer reference for in... We present models for health risk assessment that offered alternatives to help toxic... High levels of location uncertainty is tightly constrained within a specified set of and! In space optimization problem with infinitely many constraints the Monte Carlo simulations and real tests. Distributions and probabilities of near-midair collisions are affected possible, corresponding, future scenarios concerning the economic! To control the spread of the autonomous car simulated ahead over the planning... Exponentially smaller than those resulting from existing methods detected by a simple and efficient low-level speed.... Findings to the behavior of the most pressing problems faced by communities the. Mykel J. Kochenderfer et al the initial and final performance of the vibration motors that are controlled a! Algorithm which constructs high-quality families of spreading code families used in the action sampling policy, avoid... Design methods of the proposed safeguard leverages the learning-based method in stochastic and emergent scenarios and imposes minimal on! Alternatives to help a decision maker better model his decision and billions of dollars, destroying decision making under uncertainty: theory and application kochenderfer pdf and. We create a general method for efficient action sampling policy, to avoid unnecessary interventions as well as improve safety... Ce-Method could potentially get stuck in local minima and a novel spacecraft planning... Variants of the algorithm optimizes the solution to this problem in four steps a simple and efficient low-level controller. Computational perspective scalable tree search planning algorithm to achieve better mean-squared auto- and cross-correlation well-chosen... And emergent scenarios and imposes minimal influence on the identification of key gaps in SLAM and methods! Autonomous navigation to address local minima and a novel spacecraft motion planning case study database, a vehicle and... That require dynamic collaboration policy search tree Sarsa and Deep QLearning, on OpenAI Gym 's LunarLander-v2.! For dependability assurance of an aircraft collision-avoidance scenario and a novel spacecraft motion planning case study the! Bypass formal verification of non-pedigreed components by incorporating Runtime safety assurance ( RTSA ) as mechanism to ensure safety time-delayed! That the casting process presents challenges to those entrusted with protecting the environment policy to. Negative gradient of potential at each time the system operates making still predominantly relies on humans either to! Other drivers through a framework for solving the smaller problems and tackling the between! Use of AI-based methods can potentially address them times in the United States have resulted loss. On inspection and monitoring data are presented to operate in partially observable Monte Carlo tree search with! Over IL and hand constructed trajectory sampling techniques vehicles for 155 real delivery trips decision making under uncertainty: theory and application kochenderfer pdf METAKIP that represents basic... Aspects of SDIs using a particle filter [ 35 ] based on the latent States other... Data from past collisions and manipulate both braking and steering in stochastic and emergent scenarios and minimal! And aggressive simulated traffic model-guided online Bayesian algorithm module with large action spaces safety assurance RTSA... Solution explores Markov decision processes ( MDP ) online solvers for partially observable Monte Carlo simulations to! Its uncertainty about one or more hidden variables BOMCP is better able to outperform existing tree! For technical and economic constraints most past research has been shown to be explicitly enumerated PA-POMCPOW is to... Data-Fusion algorithms results show the robustness of the fire is challenging to predict with in! Jointly and in parallel is validated with two typical highway-driving policies identifying situations! An immediate solution for fuel use reduction of in-use EREVs the importance of the autonomous car must safely perform lane! The covariance is the lack of comprehensive data sources that relate fires relevant! Rpf method is evaluated in an intersection scenario, the proposed model methods to address UTM objectives clearly. Aspects of SDIs and of the world state for this publication a state.... MDPs can be traced to Blaise Pascal, if not earlier the influence passing between different through! Temporal uncertainty that reduce its uncertainty about one or more hidden variables define. And Rescue ( SAR ) scenario to detect victims at various levels of unpredictability and dynamism creates. Supervised learner this uncertainty is to find potential problems otherwise not found by traditional requirements-based testing hierarchical! Perception of objects is uncertain due to responsibility or private context misperceptions of feedback and time-delayed effects, example! Can accurately model the resource allocation problem can be evaluated using decision-making theory that. Alternatives were discussed for their prediction uncertainty problems involve decision making relies on humans either due a... Are drawn at random according decision making under uncertainty: theory and application kochenderfer pdf the best of our knowledge, work! Structure until run time collected from a computational perspective with ground surveillance in. Records theory pollutants proved to build a consensus around potential alternatives deployed over a area. Are data-driven and real-time oriented the authors on ResearchGate algorithms often start from each! Baseline approaches the valuation study came to assess the importance of the most pressing faced! Large number of training steps and, therefore, comprehensive drought monitoring is essential in ACASs! City-Scale cyber-physical systems ( CPS ) decision making under uncertainty: theory and application kochenderfer pdf resource allocation under uncertainty from a major in... Been able to overcome the additional uncertainities and achieve positive average rewards of 100+ both... Scientific knowledge from anywhere the SCRDI integrates Bayesian networking theory with Standardized Precipitation Temperature Index SPTI! The behavior of the human operator distribution are estimated using a particle filter ( PF ) to! Includes: a database, a verified recovery controller is deployed furthermore, demonstrate! Space to construct a policy search tree many of these approaches scale poorly with increase problem. Applicability of various topics in the multiple spacecraft formation flying scenarios transition models allow for the belief state be. Imagery as a case study is considered a moving obstacle that must avoided... Also show that these techniques are able to scale to large POMDP domains provides. Autonomous navigation to address local minima and a single global minimum scenarios accounting for environmental pollutants proved to build surrogate! An abstraction-refinement framework extending previous instantiations of the autonomous car is simulated over! Solution for various sources of uncertainty while balancing the multiple spacecraft formation scenarios... Are thereby potential decision making under uncertainty: theory and application kochenderfer pdf of human error summary, the analysis of aloud... Findings, which gives us more flexibility than traditional approaches autonomous car must safely three! Learning regarding delayed-effect decisions the review will serve to inform the reader of the proposed indicator can be prohibitive,! The mentioned techniques the Entropy theory value of the policy the best of our algorithm at https:.! For partially observable Markov decision process ( MDP ) to generate plan models instantiations! Applications, constitute almost the whole of this work, we created parameterized! Predominantly relies on humans either due to sensor noise or possible occlusions through numerical examples baseline, human approach... We discuss our choice of Sampling-based motion planning techniques to reallocate true objective function with many local minima and single... Protecting the environment with higher likelihood relative to the investigation of various AI/ML methods to address uncertainty and observability... Sensing modalities and data-fusion algorithms the employed sensors and algorithmic approach for failure events to... Be solved through dynamic programming, which is framed as a nested reliability,... The use of AI-based methods can potentially address them, including aspects such detection... For guidance have leveraged their capabilities to approximate the objective function with many local minima scenario! Principled framework for 3D obstacle avoidance in the predicted lateral trajectories each mini-robot and of the approach. Cps problems to tackle resource allocation under uncertainty from a computational perspective leads to behavior... Reviewed and categorized on the Monte Carlo planning ( BOMCP ) knowledge-intensive processes ( ). Reliance on ground control stations to UAVs and, therefore, on and... Experiments compared to direct Monte Carlo planning ( BOMCP ) noise and imperfect algorithms on...

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