Inductive Logic Programming: 30th International Conference, ILP 2021, Virtual Event, October 25-27, 2021, Proceedings

The 30th International Conference on Inductive Logic Programming,ILP 2021,was held virtually in October 2021,with 16 papers and 3 short papers presented. It is a subfield of machine learning that provides an excellent means for multi-relational learning and data mining.

Format: Paperback / softback
Length: 283 pages
Publication date: 24 February 2022
Publisher: Springer Nature Switzerland AG

The 30th International Conference on Inductive Logic Programming (ILP) was held virtually in October 2021 due to the COVID-19 pandemic. The conference featured a total of 16 papers and 3 short papers, which were carefully reviewed and selected from 19 submissions.

Inductive Logic Programming (ILP) is a subfield of machine learning that originally relied on logic programming as a uniform representation language for expressing examples, background knowledge, and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.

The conference proceedings cover a wide range of topics in ILP, including theoretical developments, algorithms, and applications. Some of the key themes discussed include:

Representation Learning: ILP has developed powerful representation learning techniques that allow machines to learn complex and high-dimensional data representations. These techniques are based on first-order logic and can handle a wide range of tasks, such as image classification, natural language processing, and recommendation systems.

Data Mining: ILP is widely used in data mining for tasks such as pattern discovery, association rule mining, and anomaly detection. Its ability to handle complex data and perform multi-relational learning makes it an effective tool for extracting valuable insights from large datasets.

Combinatorial Optimization: ILP has been applied to combinatorial optimization problems, such as job scheduling, transportation planning, and resource allocation. Its ability to model and solve complex optimization problems with logical constraints is a significant advantage over traditional optimization methods.

The conference also included several keynote presentations and panel discussions, which provided insights into the latest research trends and challenges in ILP. The speakers were drawn from leading institutions and research groups around the world, and their presentations covered a wide range of topics, from theoretical foundations to practical applications.

Overall, the 30th International Conference on Inductive Logic Programming was a successful event that brought together researchers and practitioners from around the world to discuss the latest developments in this exciting field. The conference proceedings will be a valuable resource for future research and development in ILP.
The 30th International Conference on Inductive Logic Programming (ILP) was held virtually in October 2021 due to the COVID-19 pandemic. The conference featured a total of 16 papers and 3 short papers, which were carefully reviewed and selected from 19 submissions.

Inductive Logic Programming (ILP) is a subfield of machine learning that originally relied on logic programming as a uniform representation language for expressing examples, background knowledge, and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.

The conference proceedings cover a wide range of topics in ILP, including theoretical developments, algorithms, and applications. Some of the key themes discussed include:

Representation Learning: ILP has developed powerful representation learning techniques that allow machines to learn complex and high-dimensional data representations. These techniques are based on first-order logic and can handle a wide range of tasks, such as image classification, natural language processing, and recommendation systems.

Data Mining: ILP is widely used in data mining for tasks such as pattern discovery, association rule mining, and anomaly detection. Its ability to handle complex data and perform multi-relational learning makes it an effective tool for extracting valuable insights from large datasets.

Combinatorial Optimization: ILP has been applied to combinatorial optimization problems, such as job scheduling, transportation planning, and resource allocation. Its ability to model and solve complex optimization problems with logical constraints is a significant advantage over traditional optimization methods.

The conference also included several keynote presentations and panel discussions, which provided insights into the latest research trends and challenges in ILP. The speakers were drawn from leading institutions and research groups around the world, and their presentations covered a wide range of topics, from theoretical foundations to practical applications.

Overall, the 30th International Conference on Inductive Logic Programming was a successful event that brought together researchers and practitioners from around the world to discuss the latest developments in this exciting field. The conference proceedings will be a valuable resource for future research and development in ILP.

Weight: 456g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030974534
Edition number: 1st ed. 2022