Agent mining

Last updated

Agent mining is an interdisciplinary area that synergizes multiagent systems with data mining and machine learning. [1] [2]

The interaction and integration between multiagent systems and data mining have a long history. [3] [4] The very early work on agent mining focused on agent-based knowledge discovery, [5] agent-based distributed data mining, [6] [7] and agent-based distributed machine learning, [8] and using data mining to enhance agent intelligence. [9]

The International Workshop on Agents and Data Mining Interaction [10] has been held for more than 10 times, co-located with the International Conference on Autonomous Agents and Multi-Agent Systems. Several proceedings are available from Springer Lecture Notes in Computer Science.

Related Research Articles

Distributed artificial intelligence (DAI) also called Decentralized Artificial Intelligence is a subfield of artificial intelligence research dedicated to the development of distributed solutions for problems. DAI is closely related to and a predecessor of the field of multi-agent systems.

The Foundation for Intelligent Physical Agents (FIPA) is a body for developing and setting computer software standards for heterogeneous and interacting agents and agent-based systems.

In computer science, a software agent is a computer program that acts for a user or another program in a relationship of agency.

<span class="mw-page-title-main">Multi-agent system</span> Built of multiple interacting agents

A multi-agent system is a computerized system composed of multiple interacting intelligent agents. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning. With advancements in Large language model (LLMs), LLM-based multi-agent systems have emerged as a new area of research, enabling more sophisticated interactions and coordination among agents.

The expression computational intelligence (CI) usually refers to the ability of a computer to learn a specific task from data or experimental observation. Even though it is commonly considered a synonym of soft computing, there is still no commonly accepted definition of computational intelligence.

The belief–desire–intention software model (BDI) is a software model developed for programming intelligent agents. Superficially characterized by the implementation of an agent's beliefs, desires and intentions, it actually uses these concepts to solve a particular problem in agent programming. In essence, it provides a mechanism for separating the activity of selecting a plan from the execution of currently active plans. Consequently, BDI agents are able to balance the time spent on deliberating about plans and executing those plans. A third activity, creating the plans in the first place (planning), is not within the scope of the model, and is left to the system designer and programmer.

In predictive analytics, data science, machine learning and related fields, concept drift or drift is an evolution of data that invalidates the data model. It happens when the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways. This causes problems because the predictions become less accurate as time passes. Drift detection and drift adaptation are of paramount importance in the fields that involve dynamically changing data and data models.

Data preprocessing can refer to manipulation, filtration or augmentation of data before it is analyzed, and is often an important step in the data mining process. Data collection methods are often loosely controlled, resulting in out-of-range values, impossible data combinations, and missing values, amongst other issues.

CALO was an artificial intelligence project that attempted to integrate numerous AI technologies into a cognitive assistant. CALO is an acronym for "Cognitive Assistant that Learns and Organizes". The name was inspired by the Latin word "Calo" which means "soldier's servant". The project started in May 2003 and ran for five years, ending in 2008.

<span class="mw-page-title-main">Juan Pavón</span> Spanish computer scientist (b.1962)

Juan Pavón is a Spanish computer scientist, full professor of the Complutense University of Madrid (UCM). He is a pioneer researcher in the field of Software Agents, co-creator of the FIPA MESSAGE and INGENIAS methodologies, and founder and director of the research group GRASIA: GRoup of Agent-based, Social and Interdisciplinary Applications at UCM. He is known for his work in the field of Artificial Intelligence, specifically in agent-oriented software engineering. He has been often cited by mainstream media, as a reference in Artificial Intelligence.

Inductive programming (IP) is a special area of automatic programming, covering research from artificial intelligence and programming, which addresses learning of typically declarative and often recursive programs from incomplete specifications, such as input/output examples or constraints.

Bing Liu is a Chinese-American professor of computer science who specializes in data mining, machine learning, and natural language processing. In 2002, he became a scholar at University of Illinois at Chicago. He holds a PhD from the University of Edinburgh (1988). His PhD advisors were Austin Tate and Kenneth Williamson Currie, and his PhD thesis was titled Reinforcement Planning for Resource Allocation and Constraint Satisfaction.

Behavior informatics (BI) is the informatics of behaviors so as to obtain behavior intelligence and behavior insights. BI is a research method combining science and technology, specifically in the area of engineering. The purpose of BI includes analysis of current behaviors as well as the inference of future possible behaviors. This occurs through pattern recognition.

Longbing Cao is an AI and data science researcher at the University of Technology Sydney, Australia. His broad research interest involves artificial intelligence, data science, behavior informatics, and their enterprise applications.

Domain driven data mining is a data mining methodology for discovering actionable knowledge and deliver actionable insights from complex data and behaviors in a complex environment. It studies the corresponding foundations, frameworks, algorithms, models, architectures, and evaluation systems for actionable knowledge discovery.

Michael Genesereth is an American logician and computer scientist, who is most known for his work on computational logic and applications of that work in enterprise management, computational law, and general game playing. Genesereth is professor in the Computer Science Department at Stanford University and a professor by courtesy in the Stanford Law School. His 1987 textbook on Logical Foundations of Artificial Intelligence remains one of the key references on symbolic artificial intelligence. He is the author of the influential Game Description Language (GDL) and Knowledge Interchange Format (KIF), the latter of which led to the ISO Common Logic standard.

<span class="mw-page-title-main">Drones in wildfire management</span> Use of drones/UAS/UAV in wildfire suppression and management

Drones, also known as Unmanned Aerial Systems/Vehicles (UAS/UAV), or Remotely Piloted Aircraft, are used in wildfire surveillance and suppression. They help in the detection, containment, and extinguishing of fires. They are also used for locating a hot spot, firebreak breaches, and then to deliver water to the affected site. In terms of maneuverability, these are superior to a helicopter or other forms of manned aircraft. They help firefighters determine where a fire will spread through tracking and mapping fire patterns. These empower scientists and incident personnel to make informed decisions. These devices can fly when and where manned aircraft are unable to fly. They are associated with low cost and are flexible devices that offer a high spatiotemporal resolution.

<span class="mw-page-title-main">Margarita Chli</span> Greek computer vision and robotics researcher

Margarita Chli is an assistant professor and leader of the Vision for Robotics Lab at ETH Zürich in Switzerland. Chli is a leader in the field of computer vision and robotics and was on the team of researchers to develop the first fully autonomous helicopter with onboard localization and mapping. Chli is also the Vice Director of the Institute of Robotics and Intelligent Systems and an Honorary Fellow of the University of Edinburgh in the United Kingdom. Her research currently focuses on developing visual perception and intelligence in flying autonomous robotic systems.

Spatial embedding is one of feature learning techniques used in spatial analysis where points, lines, polygons or other spatial data types. representing geographic locations are mapped to vectors of real numbers. Conceptually it involves a mathematical embedding from a space with many dimensions per geographic object to a continuous vector space with a much lower dimension.

References

  1. ↑ "Special issue on Agent mining, Autonomous Agents and Multi-Agent Systems". 2012.
  2. ↑ Cao, Longbing; Weiss, Gerhard; Yu, Philip (2012). "A Brief Introduction to Agent Mining". Autonomous Agents and Multi-Agent Systems. 25 (3): 419–424. doi:10.1007/s10458-011-9191-4. S2CID   7825848.
  3. ↑ Cao, Longbing; Gorodetsky, Vladimir; Mitkas, Pericles A. (2009). "Special issue on Agents and Data Mining, IEEE Intelligent Systems". IEEE Intelligent Systems. 24 (3): 14–15. doi:10.1109/MIS.2009.54.
  4. ↑ Cao, Longbing; Gorodetsky, Vladimir; Mitkas, Pericles A. (2009). "Agent Mining: The Synergy of Agents and Data Mining". IEEE Intelligent Systems. 24 (3): 64–72. doi:10.1109/MIS.2009.45. S2CID   15814593.
  5. ↑ Davies, W.; Edwards, P. (1995). "Agent-Based Knowledge Discovery". Working Notes of the AAAI Spring Symp. Information Gathering from Heterogeneous, Distributed Environments: 34–37.
  6. ↑ Klusch, M.; Lodi, S.; Moro, G. (2003). "Agent Based Distributed Data Mining". Agent-Based Distributed Data Mining. Lecture Notes in Computer Science. Vol. 2586. pp. 104–122. doi:10.1007/978-3-540-30501-9_11. ISBN   978-3-540-24013-6.
  7. ↑ Kargupta, H.; Hamzaoblu, I.; Stafford, B. (1997). "Scalable Distributed Data Mining Using an Agent-Based Architecture". Proc. 3rd Int'l Conf. Knowledge Discovery and Data Mining (KDD 97): 211–214.
  8. ↑ Weiss, G. (1998). "A multiagent perspective of parallel and distributed machine learning". Proceedings of the second international conference on Autonomous agents - AGENTS '98. pp. 226–230. doi:10.1145/280765.280806. ISBN   978-0897919838. S2CID   2325739.
  9. ↑ Symeonidis, A.; Mitkas, P. (2005). Agent Intelligence through Data Mining. Springer.
  10. ↑ "ADMI workshops".
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.