Agent programming in the cognitive era
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(2020) 34:37
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Agent programming in the cognitive era Rafael H. Bordini1 · Amal El Fallah Seghrouchni2 · Koen Hindriks3 · Brian Logan4 · Alessandro Ricci5
© The Author(s) 2020
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. Keywords Agent programming languages · Belief-desire-intention · Artificial intelligence · Machine learning
1 Introduction In the quest to build autonomous intelligent systems1 able to form hypotheses, make decisions, learn from data, and adapt their behaviour to changes in their environment, several large-scale organisations have begun to align around a concept known as the “Cognitive Era”. Drawing on recent results in the AI sub-field of machine learning (ML), some authors, e.g., Kelly and Hamm [83], have argued that, in the era of cognitive computing, intelligent systems will be trained using ML techniques, rather than developed by human 1
Autonomous systems here refers to systems that can effectively operate without human intervention.
* Brian Logan [email protected] Extended author information available on the last page of the article
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programmers. However, others, e.g., Marcus [99], argue that ML has limitations, and cannot form the sole basis of autonomous systems capable of intelligent behaviour in complex environments. Indeed they maintain that creating next-generation intelligent systems that sense, learn, reason, and interact with people in new ways will require pushing the boundaries of science and technology to integrate a wide range of AI capabilities. In this viewpoint paper we explore the contribut
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