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Found 6 papers, 3 papers with code
Mind the Error! Detection and Localization of Instruction Errors in Vision-and-Language Navigation
Moreover, we formally define the task of Instruction Error Detection and Localization, and establish an evaluation protocol on top of our benchmark dataset.
Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach
Partially Observable Markov Decision Processes (POMDPs) are a powerful framework for planning under uncertainty.
Learning Logic Specifications for Soft Policy Guidance in POMCP
In this paper, we use inductive logic programming to learn logic specifications from traces of POMCP executions, i. e., sets of belief-action pairs generated by the planner.
Rule-based Shielding for Partially Observable Monte-Carlo Planning
Results show that the shielded POMCP outperforms the standard POMCP in a case study in which a wrong parameter of POMCP makes it select wrong actions from time to time.
Identification of Unexpected Decisions in Partially Observable Monte-Carlo Planning: a Rule-Based Approach
We propose an iterative process of trace analysis consisting of three main steps, i) the definition of a question by means of a parametric logical formula describing (probabilistic) relationships between beliefs and actions, ii) the generation of an answer by computing the parameters of the logical formula that maximize the number of satisfied clauses (solving a MAX-SMT problem), iii) the analysis of the generated logical formula and the related decision boundaries for identifying unexpected decisions made by POMCP with respect to the original question.
POMP: Pomcp-based Online Motion Planning for active visual search in indoor environments
Our POMP method uses as input the current pose of an agent (e. g. a robot) and a RGB-D frame.
