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Found 14 papers, 3 papers with code
Data-Driven Goal Recognition Design for General Behavioral Agents
Following existing literature, we use worst-case distinctiveness ($\textit{wcd}$) as a measure of the difficulty in inferring the goal of an agent in a decision-making environment.
DR-HAI: Argumentation-based Dialectical Reconciliation in Human-AI Interactions
We present DR-HAI -- a novel argumentation-based framework designed to extend model reconciliation approaches, commonly used in human-aware planning, for enhanced human-AI interaction.
Using Simple Incentives to Improve Two-Sided Fairness in Ridesharing Systems
State-of-the-art order dispatching algorithms for ridesharing batch passenger requests and allocate them to a fleet of vehicles in a centralized manner, optimizing over the estimated values of each passenger-vehicle matching using integer linear programming (ILP).
On Exploiting Hitting Sets for Model Reconciliation
A popular approach to do this is called model reconciliation, where the agent tries to reconcile the differences in its model and the human's model such that the plan is also optimal in the human's model.
On the Relationship Between KR Approaches for Explainable Planning
In this paper, we build upon notions from knowledge representation and reasoning (KR) to expand a preliminary logic-based framework that characterizes the model reconciliation problem for explainable planning.
A Particle Swarm Inspired Approach for Continuous Distributed Constraint Optimization Problems
To address this issue, we propose a new C-DCOP algorithm, namely Particle Swarm Optimization Based C-DCOP (PCD), which is inspired by Particle Swarm Optimization (PSO), a well-known centralized population-based approach for solving continuous optimization problems.
Automatic Algorithm Selection In Multi-agent Pathfinding
In a multi-agent pathfinding (MAPF) problem, agents need to navigate from their start to their goal locations without colliding into each other.
Solving Distributed Constraint Optimization Problems Using Logic Programming
Under consideration in Theory and Practice of Logic Programming (TPLP).
Solving DCOPs with Distributed Large Neighborhood Search
The field of Distributed Constraint Optimization has gained momentum in recent years, thanks to its ability to address various applications related to multi-agent cooperation.
A Realistic Dataset for the Smart Home Device Scheduling Problem for DCOPs
The field of Distributed Constraint Optimization has gained momentum in recent years thanks to its ability to address various applications related to multi-agent cooperation.
Accelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUs
Discrete optimization is a central problem in artificial intelligence.
Distributed Constraint Optimization Problems and Applications: A Survey
The field of Multi-Agent System (MAS) is an active area of research within Artificial Intelligence, with an increasingly important impact in industrial and other real-world applications.
Logic and Constraint Logic Programming for Distributed Constraint Optimization
The field of Distributed Constraint Optimization Problems (DCOPs) has gained momentum, thanks to its suitability in capturing complex problems (e. g., multi-agent coordination and resource allocation problems) that are naturally distributed and cannot be realistically addressed in a centralized manner.
BnB-ADOPT: An Asynchronous Branch-and-Bound DCOP Algorithm
Our experimental results show that BnB-ADOPT finds cost-minimal solutions up to one order of magnitude faster than ADOPT for a variety of large DCOP problems and is as fast as NCBB, a memory-bounded synchronous DCOP search algorithm, for most of these DCOP problems.
