Adaptive and intelligent robot task planning for home service: A review
H Li, X Ding - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
The uncertainty and dynamic of home environment present great challenges to the task
planning of service robots. The nature of the home environment is highly unstructured, with a …
planning of service robots. The nature of the home environment is highly unstructured, with a …
Habit2vec: Trajectory semantic embedding for living pattern recognition in population
Recognizing representative living patterns in population is extremely valuable for urban
planning and decision making. Thanks to the growing popularity of location-based …
planning and decision making. Thanks to the growing popularity of location-based …
Generating diverse plans to handle unknown and partially known user preferences
Current work in planning with preferences assumes that user preferences are completely
specified, and aims to search for a single solution plan to satisfy these. In many real world …
specified, and aims to search for a single solution plan to satisfy these. In many real world …
[PDF][PDF] A machine learning approach for automatic student model discovery.
Student modeling is one of the key factors that affects automated tutoring systems in making
instructional decisions. A student model is a model to predict the probability of a student …
instructional decisions. A student model is a model to predict the probability of a student …
[HTML][HTML] Learning hierarchical task network domains from partially observed plan traces
Abstract Hierarchical Task Network (HTN) planning is an effective yet knowledge intensive
problem-solving technique. It requires humans to encode knowledge in the form of methods …
problem-solving technique. It requires humans to encode knowledge in the form of methods …
[HTML][HTML] Action-model acquisition for planning via transfer learning
Applying learning techniques to acquire action models is an area of intense research
interest. Most previous work in this area has assumed that there is a significant amount of …
interest. Most previous work in this area has assumed that there is a significant amount of …
Goal-based action priors
Robots that interact with people must flexibly respond to requests by planning in stochastic
state spaces that are often too large to solve for optimal behavior. In this work, we develop a …
state spaces that are often too large to solve for optimal behavior. In this work, we develop a …
A Bayesian approach for quantifying data scarcity when modeling human behavior via inverse reinforcement learning
Computational models that formalize complex human behaviors enable study and
understanding of such behaviors. However, collecting behavior data required to estimate the …
understanding of such behaviors. However, collecting behavior data required to estimate the …
[HTML][HTML] Integrating representation learning and skill learning in a human-like intelligent agent
Building an intelligent agent that simulates human learning of math and science could
potentially benefit both cognitive science, by contributing to the understanding of human …
potentially benefit both cognitive science, by contributing to the understanding of human …
Learning probabilistic hierarchical task networks as probabilistic context-free grammars to capture user preferences
We introduce an algorithm to automatically learn probabilistic hierarchical task networks
(pHTNs) that capture a user's preferences on plans by observing only the user's behavior …
(pHTNs) that capture a user's preferences on plans by observing only the user's behavior …