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Found 19 papers, 4 papers with code
Routing and Scheduling in Answer Set Programming applied to Multi-Agent Path Finding: Preliminary Report
We present alternative approaches to routing and scheduling in Answer Set Programming (ASP), and explore them in the context of Multi-agent Path Finding.
Explanations for Answer Set Programming
Different from xASP, the new system, xASP2, supports different clingo constructs like the choice rules, the constraints, and the aggregates such as #sum, #min.
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.
ASPER: Answer Set Programming Enhanced Neural Network Models for Joint Entity-Relation Extraction
The results show that our ASPER model consistently outperforms the baselines.
On Model Reconciliation: How to Reconcile When Robot Does not Know Human's Model?
The Model Reconciliation Problem (MRP) was introduced to address issues in explainable AI planning.
Answer Set Planning: A Survey
Answer Set Planning refers to the use of Answer Set Programming (ASP) to compute plans, i. e., solutions to planning problems, that transform a given state of the world to another state.
Specifying and Reasoning about CPS through the Lens of the NIST CPS Framework
This paper introduces a formal definition of a Cyber-Physical System (CPS) in the spirit of the CPS Framework proposed by the National Institute of Standards and Technology (NIST).
exp(ASPc) : Explaining ASP Programs with Choice Atoms and Constraint Rules
We present an enhancement of exp(ASP), a system that generates explanation graphs for a literal l - an atom a or its default negation ~a - given an answer set A of a normal logic program P, which explain why l is true (or false) given A and P. The new system, exp(ASPc), differs from exp(ASP) in that it supports choice rules and utilizes constraint rules to provide explanation graphs that include information about choices and constraints.
Planning with Incomplete Information in Quantified Answer Set Programming
We present a general approach to planning with incomplete information in Answer Set Programming (ASP).
Generating explanations for answer set programming applications
We present an explanation system for applications that leverage Answer Set Programming (ASP).
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.
Natural Language Generation for Non-Expert Users
It assumes that there exist some natural language sentences in the application domain and uses this repository for the natural language description.
Phylotastic: An Experiment in Creating, Manipulating, and Evolving Phylogenetic Biology Workflows Using Logic Programming
The Phylotastic project was launched two years ago as a collaboration between evolutionary biologists and computer scientists, with the goal of developing an open architecture to facilitate the creation of such analysis workflows.
Experimenting with robotic intra-logistics domains
More precisely, asprilo consists of a versatile benchmark generator, solution checker and visualizer as well as a bunch of reference encodings featuring various ASP techniques.
Solving Distributed Constraint Optimization Problems Using Logic Programming
Under consideration in Theory and Practice of Logic Programming (TPLP).
A Parallel Memory-efficient Epistemic Logic Program Solver: Harder, Better, Faster
As the practical use of answer set programming (ASP) has grown with the development of efficient solvers, we expect a growing interest in extensions of ASP as their semantics stabilize and solvers supporting them mature.
An Action Language for Multi-Agent Domains: Foundations
It also allows the specification of agents' dynamic awareness of action occurrences which has future implications on what agents' know about the world and other agents' knowledge about the world.
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.
Query Answering in Object Oriented Knowledge Bases in Logic Programming: Description and Challenge for ASP
Research on developing efficient and scalable ASP solvers can substantially benefit by the availability of data sets to experiment with.
