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Found 16 papers, 2 papers with code
Goal Recognition via Linear Programming
Goal Recognition is the task by which an observer aims to discern the goals that correspond to plans that comply with the perceived behavior of subject agents given as a sequence of observations.
Generalising Planning Environment Redesign
Most research on planning environment (re)design assumes the interested party's objective is to facilitate the recognition of goals and plans, and search over the space of environment modifications to find the minimal set of changes that simplify those tasks and optimise a particular metric.
Temporally Extended Goal Recognition in Fully Observable Non-Deterministic Domain Models
We develop the first approach capable of recognizing goals in such settings and evaluate it using different LTLf and PLTLf goals over six FOND planning domain models.
Uncertain Machine Ethical Decisions Using Hypothetical Retrospection
We propose the use of the hypothetical retrospection argumentation procedure, developed by Sven Ove Hansson to improve existing approaches to machine ethical reasoning by accounting for probability and uncertainty from a position of Philosophy that resonates with humans.
Iterative Depth-First Search for Fully Observable Non-Deterministic Planning
However, most of the existing algorithms are not robust for dealing with both non-determinism and task size.
Recognizing LTLf/PLTLf Goals in Fully Observable Non-Deterministic Domain Models
Goal Recognition is the task of discerning the correct intended goal that an agent aims to achieve, given a set of possible goals, a domain model, and a sequence of observations as a sample of the plan being executed in the environment.
Inferring Agents Preferences as Priors for Probabilistic Goal Recognition
Recent approaches to goal recognition have leveraged planning landmarks to achieve high-accuracy with low runtime cost.
Goal Recognition over Imperfect Domain Models
Over the past years, most existing approaches to goal and plan recognition have been ignoring the need to deal with imperfections regarding the domain model that formalizes the environment where autonomous agents behave.
The More the Merrier?! Evaluating the Effect of Landmark Extraction Algorithms on Landmark-Based Goal Recognition
Recent approaches to goal and plan recognition using classical planning domains have achieved state of the art results in terms of both recognition time and accuracy by using heuristics based on planning landmarks.
HAPRec: Hybrid Activity and Plan Recognizer
Computer-based assistants have recently attracted much interest due to its applicability to ambient assisted living.
An LP-Based Approach for Goal Recognition as Planning
Goal recognition aims to recognize the set of candidate goals that are compatible with the observed behavior of an agent.
Landmark-Based Approaches for Goal Recognition as Planning
The task of recognizing goals and plans from missing and full observations can be done efficiently by using automated planning techniques.
Using Sub-Optimal Plan Detection to Identify Commitment Abandonment in Discrete Environments
Assessing whether an agent has abandoned a goal or is actively pursuing it is important when multiple agents are trying to achieve joint goals, or when agents commit to achieving goals for each other.
LSTM-Based Goal Recognition in Latent Space
Approaches to goal recognition have progressively relaxed the requirements about the amount of domain knowledge and available observations, yielding accurate and efficient algorithms capable of recognizing goals.
Heuristic Approaches for Goal Recognition in Incomplete Domain Models
Recent approaches to goal recognition have progressively relaxed the assumptions about the amount and correctness of domain knowledge and available observations, yielding accurate and efficient algorithms.
Landmark-Based Plan Recognition
Recognition of goals and plans using incomplete evidence from action execution can be done efficiently by using planning techniques.
