Acting under uncertainty: discrete Bayesian models for mobile-robot navigation

By Anthony R. Cassandra, Leslie Pack Kaelbling, J. Kurien. 1996

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Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “Bayesian models have been shown to lead to great insights in assorted applications such as cognitive modeling [201,202] , ﬁshery [196,203] , gaming [204] , climate [205] , economet-rics [206] or robotics [207] .”
Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs (2008, unknown): “To date, POMDP-based planners have been used for applications as diverse as robot navigation [1, 2], ecological monitoring [3], dynamic pricing [4], and several areas of dialog management and healthcare [5, 6, 7].”
An experiment in integrated exploration (2002, methodology): “Region III which integrates localization and motion control is the field of active navigation [8, 6, 15] and sensor management [12].”
Learning reliable and efficient navigation with a humanoid (2010, background): “[25], the robot stops in order to obtain a good quality image whenever the uncertainty about its pose exceeds a threshold.”
A Probabilistic On-Line Mapping Algorithm for Teams of Mobile Robots (2001, methodology): “It is essentially equivalent to the Markov localization algorithm, a popular algorithm for mobile robot localization with known maps (Burgard et al. 1996; Kaelbling, Cassandra, and Kurien 1996; Nourbakhsh, Powers, and Birchfield 1995; Simmons and Koenig 1995).”
Finding Approximate POMDP solutions Through Belief Compression (2011, unknown): “The improved version of this heuristic is the Weighted-Entropy heuristic ( Cassandra et al., 1996 ).”
Active Markov localization for mobile robots (1998, methodology): “In [11] this method is extended by actions allowing the robot to improve its position estimation.”
Probabilistic Algorithms and the Interactive Museum Tour-Guide Robot Minerva (2000, methodology): “…ηt p(ot |st , m) ∫ p(st |at−1, st−1,m) × bt−1(st−1) dst−1 (4) is often referred to as Markov localization (Burgard et al. 1996; Fox, Burgard, and Thrun 1999; Kaelbling, Cassandra, and Kurien 1996; Koenig and Simmons 1996; Simmons and Koenig 1995), but it equally represents a generalization of the…”
Adapting the Sample Size in Particle Filters Through KLD-Sampling (2003, methodology): “KEY WORDS—particle filters, robot localization, non-linear estimation”
Sidekick agents for sequential planning problems (2013, methodology): “One challenge is that the continuous space could make even representing the value function required for one step of value iteration an intractable problem; however, by leveraging the piecewise-linear and convex properties of a POMDP's value function, it is possible to use finite sets of vectors to r”
Point-Based Value Iteration for Continuous POMDPs (2006, background): “, 2005), mobile robotics (Simmons and Koenig, 1995; Cassandra et al., 1996; Theocharous and Mahadevan, 2002; Pineau et al., 2003b), preference elicitation (Boutilier, 2002), spoken-dialog systems (Roy et al.”
A Probabilistic Approach to Collaborative Multi-Robot Localization (2000, methodology): “Recently, several researchers proposed Markov localization, which enables robots to localize themselves under global uncertainty (Burgard et al., 1996; Kaelbling et al., 1996; Nourbakhsh et al., 1995; Simmons and Koenig, 1995; Konolige, 1999).”
An Exploration of Embodied Visual Exploration (2020, unknown): “The action entropy control strategy in [13] explores in order to maximize the information gained about the agent’s belief state when the certainty of the optimal action is low.”