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Agent Factory: a framework for prototyping logic-based AOP languages

2011-10-06, Russell, Sean E., Jordan, Howell, O'Hare, G. M. P. (Greg M. P.), Collier, Rem

Recent years have seen the emergence of a number of AOP languages. While these can mostly be characterized as logic-oriented languages that map situations to courses of action, they are based on a variety of concepts, resulting in obvious differences in syntax and semantics. Less obviously, the development tools and infrastructure - such as environment integration, reuse mechanisms, debugging, and IDE integration surrounding these languages also vary widely. Two drawbacks of this diversity are: a perceived lack of transferability of knowledge and expertise between languages; and a potential obscuring of the fundamental conceptual differences between languages. These drawbacks can impact on both the languages’ uptake and comparability. In this paper, we present a Common Language Framework that has emerged out of ongoing work on AOP languages that have been deployed through Agent Factory. This framework consists of a set of pre-written components for building agent interpreters, together with a set of tools that can be easily adapted to different AOP languages. Through this framework we have been able to rapidly prototype a range of different AOP languages, one of which is presented as a case study in this paper.

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AF-ABLE in the Multi Agent Contest 2009

2009-09, Jordan, Howell, Treanor, Jennifer, Lillis, David, Dragone, Mauro, Collier, Rem, O'Hare, G. M. P. (Greg M. P.)

This is the second year in which a team from University College Dublin has participated in the Multi Agent Contest. This paper describes the system that was created to participate in the contest, along with observations of the team's experiences in the contest. The system itself was built using the AFAPL agent programming language running on the Agent Factory platform. A hybrid control architecture inspired by the SoSAA strategy aided in the separation of concerns between low-level behaviours (such as movement and obstacle evasion) and higher-level planning and strategy.