Zobrazeno 1 - 10
of 202
pro vyhledávání: '"Son Tran Cao"'
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
Autor:
Nguyen, Thanh Hai, Bundas, Matthew, Son, Tran Cao, Balduccini, Marcello, Garwood, Kathleen Campbell, Griffor, Edward R.
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). It shows that using this definition, various problems related to conc
Externí odkaz:
http://arxiv.org/abs/2201.05710