A Future for Agent Programming
Brian S. Logan · Lecture notes in computer science · 2015
There has been considerable progress in both the theory and practice of agent programming since Georgeff & Rao’s seminal work on the Belief-Desire-Intention paradigm. However, despite increasing interest in the development of autonomous systems, applications of agent programming are confined to a small number of niche areas, and adoption of agent programming languages in mainstream software development remains limited. This state of affairs is widely acknowledged within the community, and a number of remedies have been proposed. In this paper, I will offer one more. Starting from the class of problems agent programming sets out to solve, I will argue that a combination of Moore’s Law and advances elsewhere in AI, mean that key assumptions underlying the design of many BDI-based agent programming languages no longer hold. As a result, we are now in a position where we can rethink the foundations of BDI programming languages, and address some of the key challenges in agent development that have been largely ignored for the last twenty years. By doing so, I believe we can create theories and languages that are much more powerful and easy to use, and significantly broaden the impact of the work we do. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.