Zobrazeno 1 - 10
of 286
pro vyhledávání: '"SON, TRAN CAO"'
Gradual semantics have demonstrated great potential in argumentation, in particular for deploying quantitative bipolar argumentation frameworks (QBAFs) in a number of real-world settings, from judgmental forecasting to explainable AI. In this paper,
Externí odkaz:
http://arxiv.org/abs/2410.22209
Autor:
Uddin, Md Nayem, Saeidi, Amir, Handa, Divij, Seth, Agastya, Son, Tran Cao, Blanco, Eduardo, Corman, Steven R., Baral, Chitta
This paper introduces UnSeenTimeQA, a novel data contamination-free time-sensitive question-answering (TSQA) benchmark. It differs from existing TSQA benchmarks by avoiding web-searchable queries grounded in the real-world. We present a series of tim
Externí odkaz:
http://arxiv.org/abs/2407.03525
Reasoning about Actions and Change (RAC) has historically played a pivotal role in solving foundational AI problems, such as the frame problem. It has driven advancements in AI fields, such as non-monotonic and commonsense reasoning. RAC remains cruc
Externí odkaz:
http://arxiv.org/abs/2406.04046
Explanation generation frameworks aim to make AI systems' decisions transparent and understandable to human users. However, generating explanations in uncertain environments characterized by incomplete information and probabilistic models remains a s
Externí odkaz:
http://arxiv.org/abs/2405.19229
We present alternative approaches to routing and scheduling in Answer Set Programming (ASP), and explore them in the context of Multi-agent Path Finding. The idea is to capture the flow of time in terms of partial orders rather than time steps attach
Externí odkaz:
http://arxiv.org/abs/2403.12153
Publikováno v:
EPTCS 385, 2023, pp. 27-40
The paper presents an enhancement of xASP, a system that generates explanation graphs for Answer Set Programming (ASP). Different from xASP, the new system, xASP2, supports different clingo constructs like the choice rules, the constraints, and the a
Externí odkaz:
http://arxiv.org/abs/2308.15879
We present a novel framework designed to extend model reconciliation approaches, commonly used in human-aware planning, for enhanced human-AI interaction. By adopting a structured argumentation-based dialogue paradigm, our framework enables dialectic
Externí odkaz:
http://arxiv.org/abs/2306.14694
A plethora of approaches have been proposed for joint entity-relation (ER) extraction. Most of these methods largely depend on a large amount of manually annotated training data. However, manual data annotation is time consuming, labor intensive, and
Externí odkaz:
http://arxiv.org/abs/2305.15374
Autor:
Dung, Ho Tuan, Son, Tran Cao
Publikováno v:
EPTCS 364, 2022, pp. 27-48
The Model Reconciliation Problem (MRP) was introduced to address issues in explainable AI planning. A solution to a MRP is an explanation for the differences between the models of the human and the planning agent (robot). Most approaches to solving M
Externí odkaz:
http://arxiv.org/abs/2208.03091
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. The development of efficient and scalable answer set solvers has
Externí odkaz:
http://arxiv.org/abs/2202.05793