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Found 20 papers, 6 papers with code
Optimal and Bounded Suboptimal Any-Angle Multi-agent Pathfinding
Multi-agent pathfinding (MAPF) is the problem of finding a set of conflict-free paths for a set of agents.
Safe Learning of PDDL Domains with Conditional Effects -- Extended Version
We prove that learning non-trivial safe action models with conditional effects may require an exponential number of samples.
Enhancing Numeric-SAM for Learning with Few Observations
It runs in polynomial time and is guaranteed to output an action model that is safe, in the sense that plans generated by it are applicable and will achieve their intended goals.
A Domain-Independent Agent Architecture for Adaptive Operation in Evolving Open Worlds
Model-based reasoning agents are ill-equipped to act in novel situations in which their model of the environment no longer sufficiently represents the world.
Heuristic Search For Physics-Based Problems: Angry Birds in PDDL+
This paper studies how a domain-independent planner and combinatorial search can be employed to play Angry Birds, a well established AI challenge problem.
Learning to Operate in Open Worlds by Adapting Planning Models
Planning agents are ill-equipped to act in novel situations in which their domain model no longer accurately represents the world.
An Example of the SAM+ Algorithm for Learning Action Models for Stochastic Worlds
In this technical report, we provide a complete example of running the SAM+ algorithm, an algorithm for learning stochastic planning action models, on a simplified PPDDL version of the Coffee problem.
Playing Angry Birds with a Domain-Independent PDDL+ Planner
This demo paper presents the first system for playing the popular Angry Birds game using a domain-independent planner.
Safe Learning of Lifted Action Models
However, model learning approaches frequently do not provide safety guarantees: the learned model may assume actions are applicable when they are not, and may incorrectly capture actions' effects.
Partial Disclosure of Private Dependencies in Privacy Preserving Planning
In collaborative privacy preserving planning (CPPP), a group of agents jointly creates a plan to achieve a set of goals while preserving each others' privacy.
Improving Continuous-time Conflict Based Search
Conflict-Based Search (CBS) is a powerful algorithmic framework for optimally solving classical multi-agent path finding (MAPF) problems, where time is discretized into the time steps.
Anomaly Detection for Aggregated Data Using Multi-Graph Autoencoder
In this research, we present a thorough analysis of the aggregated data and the relationships between aggregated events.
Revisiting Bounded-Suboptimal Safe Interval Path Planning
Safe-interval path planning (SIPP) is a powerful algorithm for finding a path in the presence of dynamic obstacles.
Bidding in Spades
We present a Spades bidding algorithm that is superior to recreational human players and to publicly available bots.
Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks
The MAPF problem is the fundamental problem of planning paths for multiple agents, where the key constraint is that the agents will be able to follow these paths concurrently without colliding with each other.
Multi-Agent Pathfinding with Continuous Time
Multi-Agent Pathfinding (MAPF) is the problem of finding paths for multiple agents such that every agent reaches its goal and the agents do not collide.
Modifying Optimal SAT-based Approach to Multi-agent Path-finding Problem to Suboptimal Variants
In multi-agent path finding (MAPF) the task is to find non-conflicting paths for multiple agents.
Efficient, Safe, and Probably Approximately Complete Learning of Action Models
In this paper we explore the theoretical boundaries of planning in a setting where no model of the agent's actions is given.
Sequential Plan Recognition
The paper defines the sequential plan recognition process (SPRP), which seeks to reduce the number of hypotheses using a minimal number of queries.
