Distributed artificial intelligence Distributed Artificial Intelligence (DAI) is a subfield of artificial intelligence research dedicated to the development of distributed solutions for complex problems regarded as requiring intelligence.DAI is closely related to and a predecessor of the field of Multi-Agent Systems.

Distributed Artificial Intelligence (DAI) has existed as a subfield of AI for less than two decades. DAI is concerned with systems that consist of multiple independent entities that interact in a domain. Traditionally, DAI has been divided into two sub-disciplines: Distributed Problem Solving (DPS) focusses on the information management aspects of systems with several branches working together towards a common goal; Multiagent Systems (MAS) deals with behavior management in collections of several independent entities, or agents. This survey of MAS is intended to serve as an introduction to the field and as an organizational framework. A series of increasingly complex general multiagent scenarios are presented.

Sentient's mission is to transform how businesses tackle their most complex, mission critical problems by empowering them to make the right decisions faster. Sentient's technology has patented evolutionary and perceptual capabilities that will provide customers with highly sophisticated solutions, powered by the largest compute grid dedicated to distributed artificial intelligence. For more information, visit www.sentient.ai Utilizing evolutionary computation and deep learning – designed to continuously evolve and improve – Sentient aims to create the world's most powerful intelligent system. This system enables researchers, innovators and companies to solve mission- critical, high value, problems.

Our work aims to support decision making in situations where the source of the information on which decisions are based is of varying trustworthiness. Our approach uses formal argumentation to capture the relationships between such information sources and conclusions drawn from them. This allows the decision maker to explore how information from particular sources impacts the decisions they have to make. We describe the formal system that underlies our work, and a prototype implementation of that system, applied to a problem from military decision making.

David L. Waltz (1943-2012), was director, Center for Computational Learning Systems In 1973, Dave Waltz with Richard P. Gabriel in tow headed Dave Waltz delivers his AAAI Presidential Address at AAAI-98 in Madison, Wisconsin. While at Illinois, Dave produced system, paving the way for an engineering-style 11 Ph.D. students and many more MS students, approach to emergent AI techniques; and even mentored junior researchers and postdocs, attracted though their first attempts to create a multidisciplinary new AI faculty, and helped create the Beckman AI degree program failed, Dave was able in Institute for Advanced Science and Technology. In 1984, Marvin Minsky asked Dave to return to During the late 1970s and early 1980s, Waltz's Thinking Machines, Inc., an MIT spinoff in Cambridge group explored new ideas in natural language processing, -- with the temptation that the atmosphere cognitive science, qualitative reasoning, would be like the early days of the AI Lab all over and parallel computation in a collaborative environment again. At the same time he took a parttime including researchers in computer science, tenured position at Brandeis. Machines and Brandeis, Dave developed the ideas He chaired and brought the influential of massively parallel AI and, with Craig Stanfill, the Theoretical Issues in Natural Language Processing memory-based reasoning approach to case-based conference to Urbana in 1978.

As the title indicates, Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence covers the design and development of multiagent and distributed AI systems. The purpose of this book is to provide a comprehensive overview of the field. It is an excellent collection of closely related papers that provides a wonderful introduction to multiagent systems and distributed AI.

Thirteenth International Distributed AI Workshop

This article discusses the Thirteenth International Distributed AI Workshop. An overview of the workshop is given as well as concerns and goals for the technology. This article discusses the Thirteenth International Distributed AI Workshop. An overview of the workshop is given as well as concerns and goals for the technology.

The goal of this workshop was which was held in June 1995 in San istributed artificial intelligence the cooperative solution of "making connections," trying to better Francisco. The DAI Workshop problems in multiagent intelligent understand the connections received financial support from the systems with both computational between DAI and related fields (for American Association for Artificial and human agents. The central problem example, computer-supported cooperative Intelligence as well as the Boeing in DAI is how to achieve coordinated work, group decision support Company. Registration materials for the Thirteenth National Conference on Artificial Intelligence (AAAI-96), the Eighth Innovative Applications of Artificial Intelligence Conference (IAAI-96), and the Second International Conference on Knowledge Discovery and Data Mining (KDD-96) are now available from the AAAI office at ncai@aaai.org Copies of the AAAI-96 registration brochure are being mailed to all AAAI members.

This report contains a historical perspective on previous Distributed Artificial Intelligence Workshops, highlights of the roundtable discussions, and a collection of research abstracts submitted by the participants.