Meta-Information and Argumentation in Multi-Agent Systems

In this work we compile our research regarding meta-information in multi-agent systems. In particular, we describe some agents profiles represent- ing different attitudes which describe how agents consider meta-information in their decisions-making and reasoning processes. Furthermore, we describe how we have combined different meta-information available in multi-agent systems with an argumentation-based reasoning mechanism. In our approach, agents are able to decide more conflicts between information/arguments, given that they are able to use different meta-information (often combined) to decide between such conflicting information. Our framework for meta-information in multi- agent systems was implemented based on a modular architecture, thus other meta-information can be added, as well as different meta-information can be combined in order to create new agents profiles. Therefore, in our approach, different agents profiles can be instantiated for different application domains, allowing flexibility in the choice of how agents will deal with conflicting infor- mation in those particular domains

RV4JaCa - Runtime Verification for Multi-Agent Systems

This paper presents a Runtime Verification (RV) approach for Multi-Agent Systems (MAS) using the JaCaMo framework. Our objective is to bring a layer of security to the MAS. This layer is capable of controlling events during the execution of the system without needing a specific implementation in the behaviour of each agent to recognise the events. MAS have been used in the context of hybrid intelligence. This use requires communication between software agents and human beings. In some cases, communication takes place via natural language dialogues. However, this kind of communication brings us to a concern related to controlling the flow of dialogue so that agents can prevent any change in the topic of discussion that could impair their reasoning. We demonstrate the implementation of a monitor that aims to control this dialogue flow in a MAS that communicates with the user through natural language to aid decision-making in hospital bed allocation

Explaining Semantic Reasoning Using Argumentation

Multi-Agent Systems (MAS) are popular because they provide a paradigm that naturally meets the current demand to design and implement distributed intelligent systems. When developing a multi-agent application, it is common to use ontologies to provide the domain-specific knowledge and vocabulary necessary for agents to achieve the system goals. In this paper, we propose an approach in which agents can query semantic reasoners and use the received inferences to build explanations for such reasoning. Also, thanks to an internal representation of inference rules used to build explanations, in the form of argumentation schemes, agents are able to reason and make decisions based on the answers from the semantic reasoner. Furthermore, agents can communicate the built explanation to other agents and humans, using computational or natural language representations of arguments. Our approach paves the way towards multi-agent systems able to provide explanations from the reasoning carried out by semantic reasoners

Sexual function in 16- to 21-year-olds in Britain

Purpose: Concern about young people's sexuality is focused on the need to prevent harmful outcomes such as sexually transmitted infections and unplanned pregnancy. Although the benefit of a broader perspective is recognized, data on other aspects of sexuality, particularly sexual function, are scant. We sought to address this gap by measuring the population prevalence of sexual function problems, help seeking, and avoidance of sex in young people. Methods: A cross-sectional stratified probability sample survey (Natsal-3) of 15,162 women and men in Britain (response rate: 57.7%), using computer-assisted self-interviews. Data come from 1875 (71.9%) sexually active, and 517 sexually inactive (18.7%), participants aged 16–21 years. Measures were single items from a validated measure of sexual function (the Natsal-SF). Results: Among sexually active 16- to 21-year-old participants, 9.1% of men and 13.4% of women reported a distressing sexual problem lasting 3 months or more in the last year. Most common among men was reaching a climax too quickly (4.5%), and among women was difficulty in reaching climax (6.3%). Just over a third (35.5%) of men and 42.3% of women reporting a problem had sought help, but rarely from professional sources. Among those who had not had sex in the last year, just >10% of young men and women said they had avoided sex because of sexual difficulties. Conclusions: Distressing sexual function problems are reported by a sizeable minority of sexually active young people. Education is required, and counseling should be available, to prevent lack of knowledge, anxiety, and shame progressing into lifelong sexual difficulties

Practical Verification of Decision-Making in Agent-Based Autonomous Systems

We present a verification methodology for analysing the decision-making component in agent-based hybrid systems. Traditionally hybrid automata have been used to both implement and verify such systems, but hybrid automata based modelling, programming and verification techniques scale poorly as the complexity of discrete decision-making increases making them unattractive in situations where complex log- ical reasoning is required. In the programming of complex systems it has, therefore, become common to separate out logical decision-making into a separate, discrete, component. However, verification techniques have failed to keep pace with this devel- opment. We are exploring agent-based logical components and have developed a model checking technique for such components which can then be composed with a sepa- rate analysis of the continuous part of the hybrid system. Among other things this allows program model checkers to be used to verify the actual implementation of the decision-making in hybrid autonomous systems

Context-dependent combination of sensor information in Dempster–Shafer theory for BDI

© 2016, The Author(s). There has been much interest in the belief–desire–intention (BDI) agent-based model for developing scalable intelligent systems, e.g. using the AgentSpeak framework. However, reasoning from sensor information in these large-scale systems remains a significant challenge. For example, agents may be faced with information from heterogeneous sources which is uncertain and incomplete, while the sources themselves may be unreliable or conflicting. In order to derive meaningful conclusions, it is important that such information be correctly modelled and combined. In this paper, we choose to model uncertain sensor information in Dempster–Shafer (DS) theory. Unfortunately, as in other uncertainty theories, simple combination strategies in DS theory are often too restrictive (losing valuable information) or too permissive (resulting in ignorance). For this reason, we investigate how a context-dependent strategy originally defined for possibility theory can be adapted to DS theory. In particular, we use the notion of largely partially maximal consistent subsets (LPMCSes) to characterise the context for when to use Dempster’s original rule of combination and for when to resort to an alternative. To guide this process, we identify existing measures of similarity and conflict for finding LPMCSes along with quality of information heuristics to ensure that LPMCSes are formed around high-quality information. We then propose an intelligent sensor model for integrating this information into the AgentSpeak framework which is responsible for applying evidence propagation to construct compatible information, for performing context-dependent combination and for deriving beliefs for revising an agent’s belief base. Finally, we present a power grid scenario inspired by a real-world case study to demonstrate our work