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Found 15 papers, 3 papers with code
Human Goal Recognition as Bayesian Inference: Investigating the Impact of Actions, Timing, and Goal Solvability
Goal recognition is a fundamental cognitive process that enables individuals to infer intentions based on available cues.
Generalized Planning for the Abstraction and Reasoning Corpus
The Abstraction and Reasoning Corpus (ARC) is a general artificial intelligence benchmark that poses difficulties for pure machine learning methods due to its requirement for fluid intelligence with a focus on reasoning and abstraction.
Data-Driven Goal Recognition in Transhumeral Prostheses Using Process Mining Techniques
A transhumeral prosthesis restores missing anatomical segments below the shoulder, including the hand.
Diverse, Top-k, and Top-Quality Planning Over Simulators
Diverse, top-k, and top-quality planning are concerned with the generation of sets of solutions to sequential decision problems.
Lifted Sequential Planning with Lazy Constraint Generation Solvers
This paper studies the possibilities made open by the use of Lazy Clause Generation (LCG) based approaches to Constraint Programming (CP) for tackling sequential classical planning.
Novelty and Lifted Helpful Actions in Generalized Planning
It has been shown recently that successful techniques in classical planning, such as goal-oriented heuristics and landmarks, can improve the ability to compute planning programs for generalized planning (GP) problems.
A Reminder of its Brittleness: Language Reward Shaping May Hinder Learning for Instruction Following Agents
Teaching agents to follow complex written instructions has been an important yet elusive goal.
Sampling from Pre-Images to Learn Heuristic Functions for Classical Planning
We introduce a new algorithm, Regression based Supervised Learning (RSL), for learning per instance Neural Network (NN) defined heuristic functions for classical planning problems.
Width-based Lookaheads with Learnt Base Policies and Heuristics Over the Atari-2600 Benchmark
We propose new width-based planning and learning algorithms inspired from a careful analysis of the design decisions made by previous width-based planners.
Planning for Novelty: Width-Based Algorithms for Common Problems in Control, Planning and Reinforcement Learning
Width-based algorithms search for solutions through a general definition of state novelty.
Approximate Novelty Search
Width-based search algorithms seek plans by prioritizing states according to a suitably defined measure of novelty, that maps states into a set of novelty categories.
Planimation
Planimation is a modular and extensible open source framework to visualise sequential solutions of planning problems specified in PDDL.
Novelty Messages Filtering for Multi Agent Privacy-preserving Planning
In multi-agent planning, agents jointly compute a plan that achieves mutual goals, keeping certain information private to the individual agents.
Best-First Width Search for Multi Agent Privacy-preserving Planning
In multi-agent planning, preserving the agents' privacy has become an increasingly popular research topic.
What you get is what you see: Decomposing Epistemic Planning using Functional STRIPS
Epistemic planning --- planning with knowledge and belief --- is essential in many multi-agent and human-agent interaction domains.
