Agent programming in the cognitive era

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

Resilience, reliability, and coordination in autonomous multi-agent systems

Acknowledgements The research reported in this paper was funded and supported by various grants over the years: Robotics and AI in Nuclear (RAIN) Hub (EP/R026084/1); Future AI and Robotics for Space (FAIR-SPACE) Hub (EP/R026092/1); Offshore Robotics for Certification of Assets (ORCA) Hub (EP/R026173/1); the Royal Academy of Engineering under the Chair in Emerging Technologies scheme; Trustworthy Autonomous Systems “Verifiability Node” (EP/V026801); Scrutable Autonomous Systems (EP/J012084/1); Supporting Security Policy with Effective Digital Intervention (EP/P011829/1); The International Technology Alliance in Network and Information Sciences.Peer reviewedPostprin

Future directions in agent programming

Agent programming is a subfield of Artificial Intelligence concerned with the development of intelligent autonomous systems that combine multiple capabilities, e.g., sensing, deliberation, problem-solving and action, in a single system. There has been considerable progress in both the theory and practice of agent programming since Georgeff & Rao’s seminal work on the Belief-Desire-Intention paradigm. However, despite increasing interest in the development of autonomous systems, applications of agent programming are currently confined to a small number of niche areas, and adoption of agent programming languages (APLs) in mainstream software development remains limited. In this paper, I argue that increased adoption of agent programming is contingent on being able to solve a larger class of AI problems with significantly less developer effort than is currently the case, and briefly sketch one possible approach to expanding the set of AI problems that can be addressed by APLs. Critically, the approach I propose requires minimal developer effort and expertise, and relies instead on expanding the basic capabilities of the language

Towards Consistency-Based Reliability Assessment

International audienceMOTIVATION : Merging information provided by several sources is an important issue and merging techniques have been extensively studied. When the reliability of the sources is not known, one can apply merging techniques such as majority or arbitration merging or distancebasedmerging for solving conflicts between information. At the opposite, if the reliability of the sources is known, either represented in a quantitative or in a qualitative way, then it can be used to manage contradictions: information provided by a source is generally weakened or ignored if it contradicts information provided by a more reliable source [1, 4, 6]. Assessing the reliability of information sources is thus crucial. The present paper addresses this key question. We adopt a qualitative point of view for reliability representation by assuming that the relative reliability of information sources is represented by a total preorder. This works considers that we have no information about the sources and in particular, we do not know if they are correct (i.e they provide true information) or not. We focus on a preliminary stage of observation and assessment of sources. We claim that during that stage the key issue is a consistency analysis of information provided by sources, whether it is the consistency of single reports or consistency w.r.t trusted knowledge or the consistency of different reports together. We adopt an axiomatic approach: first we give some postulates which characterize what this reliability preorder should be, then we define a generic operator for building this preorder in agreement with the postulates

Early detection of design faults relative to requirement specifications in agent-based models

Agent systems are used for a wide range of applications, and techniques to detect and avoid defects in such systems are valuable. In particular, it is desirable to detect issues as early as possible in the software development lifecycle. We describe a technique for checking the plan structures of a BDI agent design against the requirements models, specified in terms of scenarios and goals. This approach is applicable at design time, not requiring source code. A lightweight evaluation demonstrates that a range of defects can be found using this technique

Revenue and User Traffic Maximization in Mobile Short-Video Advertising

A new mobile attention economy has emerged with the explosive growth of short-video apps such as TikTok. In this internet market, three types of agents interact with each other: the platform, influencers, and advertisers. A short-video platform encourages its influencers to attract users by creating appealing content through short-form videos and allows advertisers to display their ads in short-form videos. There are two options for the advertisers: one is to bid for platform advert slots in a similar way to search engine auctions; the other is to pay an influencer to make engaging short videos and promote them through the influencer's channel. The second option will generate a higher conversion ratio if advertisers choose the right influencers whose followers match their target market. Although displaying influencer ads will generate less revenue, it is more engaging than platform ads, which is better for maintaining user traffic. Therefore, it is crucial for a platform to balance these factors by establishing a sustainable business agreement with its influencers and advertisers. In this paper, we develop a two-stage solution for a platform to maximize short-term revenue and long-term user traffic maintenance. In the first stage, we estimate the impact of user traffic generated by displaying influencer ads and characterize the user traffic the platform should allocate to influencers for overall revenue maximization. In the second stage, we devise an optimal (1 - 1/e)-competitive algorithm for ad slot allocation. To complement this analysis, we examine the ratio of the revenue generated by our online algorithm to the optimal offline revenue. Our simulation results show that this ratio is 0.94 on average, which is much higher than (1 - 1/e) and outperforms four baseline algorithms