Engineering Multi-Agent Systems: State of Affairs and the Road Ahead

The continuous integration of software-intensive systems together with the ever-increasing computing power offer a breeding ground for intelligent agents and multi-agent systems (MAS) more than ever before. Over the past two decades, a wide variety of languages, models, techniques and methodologies have been proposed to engineer agents and MAS. Despite this substantial body of knowledge and expertise, the systematic engineering of large-scale and open MAS still poses many challenges. Researchers and engineers still face fundamental questions regarding theories, architectures, languages, processes, and platforms for designing, implementing, running, maintaining, and evolving MAS. This paper reports on the results of the 6th International Workshop on Engineering Multi-Agent Systems (EMAS 2018, 14th-15th of July, 2018, Stockholm, Sweden), where participants discussed the issues above focusing on the state of affairs and the road ahead for researchers and engineers in this area

Role of pathogenic oral flora in postoperative pneumonia following brain surgery

<p>Abstract</p> <p>Background</p> <p>Post-operative pulmonary infection often appears to result from aspiration of pathogens colonizing the oral cavity. It was hypothesized that impaired periodontal status and pathogenic oral bacteria significantly contribute to development of aspiration pneumonia following neurosurgical operations. Further, the prophylactic effects of a single dose preoperative cefazolin on the oral bacteria were investigated.</p> <p>Methods</p> <p>A matched cohort of 18 patients without postoperative lung complications was compared to 5 patients who developed pneumonia within 48 hours after brain surgery. Patients waiting for elective operation of a single brain tumor underwent dental examination and saliva collection before surgery. Bacteria from saliva cultures were isolated and periodontal disease was scored according to type and severity. Patients received 15 mg/kg cefazolin intravenously at the beginning of surgery. Serum, saliva and bronchial secretion were collected promptly after the operation. The minimal inhibitory concentrations of cefazolin regarding the isolated bacteria were determined. The actual antibiotic concentrations in serum, saliva and bronchial secretion were measured by capillary electrophoresis upon completion of surgery. Bacteria were isolated again from the sputum of postoperative pneumonia patients.</p> <p>Results</p> <p>The number and severity of coexisting periodontal diseases were significantly greater in patients with postoperative pneumonia in comparison to the control group (p = 0.031 and p = 0.002, respectively). The relative risk of developing postoperative pneumonia in high periodontal score patients was 3.5 greater than in patients who had low periodontal score (p < 0.0001). Cefazolin concentration in saliva and bronchial secretion remained below detectable levels in every patient.</p> <p>Conclusion</p> <p>Presence of multiple periodontal diseases and pathogenic bacteria in the saliva are important predisposing factors of postoperative aspiration pneumonia in patients after brain surgery. The low penetration rate of cefazolin into the saliva indicates that its prophylactic administration may not be sufficient to prevent postoperative aspiration pneumonia. Our study suggests that dental examination may be warranted in order to identify patients at high risk of developing postoperative respiratory infections.</p

A cloud-based middleware for multi-modal interaction services and applications

Smart devices, such as smart phones, voice assistants and social robots, provide users with a range of input modalities, e.g., speech, touch, gestures, and vision. In recent years, advancements in processing of these input channels enable more natural interaction (e.g., automated speech, face, and gesture recognition, dialog generation, emotion expression etc.) experiences for users. However, there are several important challenges that need to be addressed to create these user experiences. One challenge is that most smart devices do not have sufficient computing resources to execute the Artificial Intelligence (AI) techniques locally. Another challenge is that users expect responses in near real-time when they interact with these devices. Moreover, users also want to be able to seamlessly switch between devices and services any time and from anywhere and expect personalized and privacy-aware services. To address these challenges, we design and develop a cloud-based middleware (CMI) which helps to develop multi-modal interaction applications and easily integrate applications to AI services. In this middleware, services developed by different producers with different protocols and smart devices with different capabilities and protocols can be integrated easily. In CMI, applications stream data from devices to cloud services for processing and consume the results. It supports data streaming from multiple devices to multiple services (and vice versa). CMI provides an integration framework for decoupling the services and devices and enabling application developers to concentrate on 'interaction' instead of AI techniques. We provide simple examples to illustrate the conceptual ideas incorporated in CMI

How to Recognize and Explain Bidding Strategies in Negotiation Support Systems

Effective use of negotiation support systems depends on the systems capability of explaining itself to the user. This paper introduces the notion of an explanation matrix and an aberration detection mechanism for bidding strategies. The aberration detection is a mechanism that detects if one of the negotiating parties deviates from their expected behaviour, i.e. when a bid falls outside the range of expected behaviour for a specific strategy. The explanation matrix is used when to explain which aberrations to the user. The idea is that the user, when understanding the aberration, can take effective action to deal with the aberration. We implemented our aberration detection and our explanation mechanisms in the Pocket Negotiator (PN). We evaluated our work experimentally in a task in which participants are asked to identify their opponent’s bidding strategy, under different explanation conditions. As the number of correct guesses increases with explanations, indirectly, these experiments show the effectiveness of our aberration detection mechanism. Our experiments with over 100 participants show that suggesting consistent strategies is more effective than explaining why observed behaviour is inconsistent. An extended abstract of this article can be found in [15].Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Interactive Intelligenc

The “why did you do that?” button: Answering why-questions for end users of robotic systems

The issue of explainability for autonomous systems is becoming increasingly prominent. Several researchers and organisations have advocated the provision of a “Why did you do that?” button which allows a user to interrogate a robot about its choices and actions. We take previous work on debugging cognitive agent programs and apply it to the question of supplying explanations to end users in the form of answers to why-questions. These previous approaches are based on the generation of a trace of events in the execution of the program and then answering why-questions using the trace. We implemented this framework in the agent infrastructure layer and, in particular, the Gwendolen programming language it supports – extending it in the process to handle the generation of applicable plans and multiple intentions. In order to make the answers to why-questions comprehensible to end users we advocate a two step process in which first a representation of an explanation is created and this is subsequently converted into natural language in a way which abstracts away from some events in the trace and employs application specific predicate dictionaries in order to translate the first-order logic presentation of concepts within the cognitive agent program in natural language. A prototype implementation of these ideas is provided

How to Recognize and Explain Bidding Strategies in Negotiation Support Systems

Effective use of negotiation support systems depends on the systems capability of explaining itself to the user. This paper introduces the notion of an explanation matrix and an aberration detection mechanism for bidding strategies. The aberration detection is a mechanism that detects if one of the negotiating parties deviates from their expected behaviour, i.e. when a bid falls outside the range of expected behaviour for a specific strategy. The explanation matrix is used when to explain which aberrations to the user. The idea is that the user, when understanding the aberration, can take effective action to deal with the aberration. We implemented our aberration detection and our explanation mechanisms in the Pocket Negotiator (PN). We evaluated our work experimentally in a task in which participants are asked to identify their opponent’s bidding strategy, under different explanation conditions. As the number of correct guesses increases with explanations, indirectly, these experiments show the effectiveness of our aberration detection mechanism. Our experiments with over 100 participants show that suggesting consistent strategies is more effective than explaining why observed behaviour is inconsistent. An extended abstract of this article can be found in [15]

Engineering multi-agent systems: State of affairs and the road ahead

The continuous integration of software-intensive systems together with the ever-increasing computing power offer a breeding ground for intelligent agents and multi-agent systems (MAS) more than ever before. Over the past two decades, a wide variety of languages, models, techniques and methodologies have been proposed to engineer agents and MAS. Despite this substantial body of knowledge and expertise, the systematic engineering of large-scale and open MAS still poses many challenges. Researchers and engineers still face fundamental questions regarding theories, architectures, languages, processes, and platforms for designing, implementing, running, maintaining, and evolving MAS. This paper reports on the results of the 6th International Workshop on Engineering Multi-Agent Systems (EMAS 2018, 14th-15th of July, 2018, Stockholm, Sweden), where participants discussed the issues above focusing on the state of affairs and the road ahead for researchers and engineers in this area