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2015 | OriginalPaper | Buchkapitel

A Future for Agent Programming

verfasst von : Brian Logan

Erschienen in: Engineering Multi-Agent Systems

Verlag: Springer International Publishing

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Abstract

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.

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Fußnoten
1
Programming frameworks for the development of MAS are an important output of the agent programming community, but are not essential for the implementation of individual autonomous systems.
 
2
The engagement of industry with the AAMAS conference as a whole also does not seem a particularly relevant metric when considering future directions for engineering multi-agent systems. AAMAS is a large conference, and there are typically only a relatively small number of papers on agent programming; even if these papers were very relevant to industry, industrial engagement with the conference as a whole could still be low.
 
3
The alternative interpretation, that they are all necessary for the wider adoption of APLs, implies that agent programming as a field must progress on a very broad front, and is even more daunting than my analysis below.
 
4
There are, of course, overlaps between the two views. In particular, there is a strand of work in what I am characterising as the AI-oriented view, that focuses on the engineering of intelligent autonomous systems. However the focus of work in the software engineering-oriented tradition is much less on AI and more on distributed systems.
 
5
The argument that an APL should include only basic plan selection features seems spurious for another reason—most widely used programming languages provide support for many more features than will be used in any particular application.
 
6
Creative Commons ‘Transistor Count and Moore’s Law’ by Wgsimon used under CC-BY-SA-3.0. Transistor counts circa 1987 and 1996 highlighted.
 
8
The figure is from http://​users.​ics.​aalto.​fi/​rintanen/​jussi/​satplan.​html and is reproduced here with the permission of the author. See also [20].
 
9
The 2014 edition of the International Planning Competition included a Sequential Agile track for the first time. The objective of the Agile track is to ‘minimize the CPU time needed for finding a plan’.
 
13
A similar point is made by Hindriks [13] when he advocates easy access to powerful AI techniques. However Hindriks sees this as a desirable rather than a necessary feature.
 
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Metadaten
Titel
A Future for Agent Programming
verfasst von
Brian Logan
Copyright-Jahr
2015
DOI
https://doi.org/10.1007/978-3-319-26184-3_1

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