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.
In this paper, we present a new protocol to address multilateral multi-issue negotiation in a cooperative context. We consider complex dependencies between multiple issues by modelling the preferences of the agents with a multi-criteria decision aid tool, also enabling us to extract relevant information on a proposal assessment. This information is used in the protocol to help in accelerating the search for a consensus between the cooperative agents. In addition, the negotiation procedure is defined in a crisis management context where the common objective of our agents is also considered in the preferences of a mediator agent.
In this paper we address the problem of distributed sources of information, or agents, that observe the environment locally and have to communicate in order to refine their hypothesis regarding the actual state of this environment. One way to address the problem would be to centralize all the collected observations and knowledge, and to centrally compute the resulting theory. In many situations however, it would not be possible to adopt this centralized approach (e.g. for practical reasons, or privacy concerns). In this paper, we assume that agents individually face abductive or inductive tasks in a globally coherent environment, and we show that general mechanisms can be designed that abstractly regard both cases as special instances of a problem of hypothesis refinement through propagation. Assuming that agents are equipped with some individual revision machinery, our concern will be to investigate how (under what conditions) convergence to a consistent state can be guaranteed at more global levels: (i) between two agents; (ii) in a clique of agents; and (iii) in general in a connected society of agents.
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