TY - CHAP
T1 - LTN
T2 - Logic Tensor Networks
AU - Shakarian, Paulo
AU - Baral, Chitta
AU - Simari, Gerardo I.
AU - Xi, Bowen
AU - Pokala, Lahari
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - In this chapter, we provide an overview of Logic Tensor Networks (LTNs, for short), a formalism that makes use of tensor embeddings—n-dimensional vector representations—of elements tied to a logical syntax, which has seen traction in NSR literature in the past few years. After briefly recalling Real Logic, the underlying language of LTNs, we discuss the representation of different kinds of knowledge in formalism, the three main tasks that can be addressed with them (learning, reasoning, and query answering), and finally, describe several use cases that have shown the usefulness of LTNs in many tasks that are central to the construction of intelligent systems.
AB - In this chapter, we provide an overview of Logic Tensor Networks (LTNs, for short), a formalism that makes use of tensor embeddings—n-dimensional vector representations—of elements tied to a logical syntax, which has seen traction in NSR literature in the past few years. After briefly recalling Real Logic, the underlying language of LTNs, we discuss the representation of different kinds of knowledge in formalism, the three main tasks that can be addressed with them (learning, reasoning, and query answering), and finally, describe several use cases that have shown the usefulness of LTNs in many tasks that are central to the construction of intelligent systems.
UR - http://www.scopus.com/inward/record.url?scp=85172419312&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85172419312&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-39179-8_4
DO - 10.1007/978-3-031-39179-8_4
M3 - Chapter
AN - SCOPUS:85172419312
T3 - SpringerBriefs in Computer Science
SP - 33
EP - 41
BT - SpringerBriefs in Computer Science
PB - Springer
ER -