No abstract

It is claimed that, in the nascent 'Cognitive Era', intelligent systems will be trained using machine learning techniques rather than programmed by software developers. A contrary point of view argues that machine learning has limitations, and, taken in isolation, cannot form the basis of autonomous systems capable of intelligent behaviour in complex environments. In this paper, we explore the contributions that agent-oriented programming can make to the development of future intelligent systems. We briefly review the state of the art in agent programming, focussing particularly on BDI-based agent programming languages, and discuss previous work on integrating AI techniques (including machine learning) in agent-oriented programming. We argue that the unique strengths of BDI agent languages provide an ideal framework for integrating the wide range of AI capabilities necessary for progress towards the next-generation of intelligent systems. We identify a range of possible approaches to integrating AI into a BDI agent architecture. Some of these approaches, e.g., 'AI as a service', exploit immediate synergies between rapidly maturing AI techniques and agent programming, while others, e.g., 'AI embedded into agents' raise more fundamental research questions, and we sketch a programme of research directed towards identifying the most appropriate ways of integrating AI capabilities into agent programs.

Abstract-Several techniques have been proposed in the last few years to address the multiagent patrolling task. They share the assumption of a closed system setting (the set of agents present in the system is constant, no agent joins or leaves), which is a strong requirement and limits the applicability of multiagent patrolling models. In this article, we propose to revisit some of the techniques proposed in the literature to adapt them to the open society setting, and to compare their performances on a simple scenario where an agent decides to quit the patrolling task.