Agent transportation layer adaptation system

Abstract

Heuristic computing has consolidated into two streams of research. One that personifies software to exhibit human behaviour and an oher that provides innovative software or smart products [1]. The Turing test [2] was pivotal in providing researchers with a generally accepted method of classifying the work that now defines the major problems pursued within Artificial Intelligence (AI). Cognitive Science is one of these fields and Research in Multi-Agent System (MAS) has revealed that Agents must enter into a voluntarily trust relationship in order to collaborate, otherwise the imposed goal(s) may be aborted or fail completely [3, 4]. Current agent architectures present a finite limit to functionality when supporting one or more of these paradigms. Discussion about a framework being developed at the University of South Australia enables individual students associated with our Knowledge-Based Intelligent Information & Engineering Systems (KES) centre to fast track the development of their research concepts via a Plug 'n' Play mechanism within a multi-agent blackboard architecture. This paper highlights the core architecture, we believe is required for MAS developers achieve such flexibility. The research focuses on how agents can be teamed to provide the ability to adapt and dynamically organise the required functionality to automate in a team environment. The model is conceptual and has been designed initially as a blackboard model, where each element represents a block of functionality required to automate a process in order to complete a specific task. Discussion is limited to the formative work within the foundation layers of that framework. © 2009 Springer-Verlag Berlin Heidelberg.