Towards the Verification of Human-Robot Teams

Human-Agent collaboration is increasingly important. Not only do high-profile activities such as NASA missions to Mars intend to employ such teams, but our everyday activities involving interaction with computational devices falls into this category. In many of these scenarios, we are expected to trust that the agents will do what we expect and that the agents and humans will work together as expected. But how can we be sure? In this paper, we bring together previous work on the verification of multi-agent systems with work on the modelling of human-agent teamwork. Specifically, we target human-robot teamwork. This paper provides an outline of the way we are using formal verification techniques in order to analyse such collaborative activities. A particular application is the analysis of human-robot teams intended for use in future space exploration

Object-Agent Oriented Programming

Object-oriented programming has been used for building intelligent agents, with the limitation it cannot represent complex mental attitudes. With logic programming it is possible to represent and infer relationships among mental attitudes such as intentions, goals and beliefs, with limitations in the usage of capabilities of action. This paper presents two alternatives for integrating object- oriented with logic programming, which enable agent programming. Java and Smalltalk have been used for providing one typed and another non-typed integration with Prolog.Sociedad Argentina de Informática e Investigación Operativ

Design and Analysis of a Multi-Agent E-Learning System Using Prometheus Design Tool

Agent unified modeling languages (AUML) are agent-oriented approaches that supports the specification, design, visualization and documentation of an agent-based system. This paper presents the use of Prometheus AUML approach for the modeling of a Pre-assessment System of five interactive agents. The Pre-assessment System, as previously reported, is a multi-agent based e-learning system that is developed to support the assessment of prior learning skills in students so as to classify their skills and make recommendation for their learning. This paper discusses the detailed design approach of the system in a step-by-step manner; and domain knowledge abstraction and organization in the system. In addition, the analysis of the data collated and models of prediction for future pre-assessment results are also presented.Comment: 17 figures, 3 table

Language support for multi agent reinforcement learning

Software Engineering must increasingly address the issues of complexity and uncertainty that arise when systems are to be deployed into a dynamic software ecosystem. There is also interest in using digital twins of systems in order to design, adapt and control them when faced with such issues. The use of multi-agent systems in combination with reinforcement learning is an approach that will allow software to intelligently adapt to respond to changes in the environment. This paper proposes a language extension that encapsulates learning-based agents and system building operations and shows how it is implemented in ESL. The paper includes examples the key features and describes the application of agent-based learning implemented in ESL applied to a real-world supply chain

Mutation for Multi-Agent Systems

Although much progress has been made in engineering multi-agent systems (MAS), many issues remain to be resolved. One issue is that there is a lack of techniques that can adequately evaluate the effectiveness (fault detection ability) of tests or testing techniques for MAS. Another is that there are no systematic approaches to evaluating the impact of possible semantic changes (changes in the interpretation of agent programs) on agents' behaviour and performance. This thesis introduces syntactic and semantic mutation to address these two issues. Syntactic mutation is a technique that systematically generates variants ("syntactic mutants") of a description (usually a program) following a set of rules ("syntactic mutation operators"). Each mutant is expected to simulate a real description fault, therefore, the effectiveness of a test set can be evaluated by checking whether it can detect each simulated fault, in other words, distinguish the original description from each mutant. Although syntactic mutation is widely considered very effective, only limited work has been done to introduce it into MAS. This thesis extends syntactic mutation for MAS by proposing a set of syntactic mutation operators for the Jason agent language and showing that they can be used to generate real faults in Jason agent programs. By contrast, semantic mutation systematically generates variant interpretations ("semantic mutants") of a description following a set of rules ("semantic mutation operators"). Semantic mutation has two uses: to evaluate the effectiveness of a test set by simulating faults caused by misunderstandings of how the description is interpreted, and to evaluate the impact of possible semantic changes on agents' behaviour and performance. This thesis, for the first time, proposes semantic mutation for MAS, more specifically, for three logic based agent languages, namely Jason, GOAL and 2APL. It proposes semantic mutation operators for these languages, shows that the operators for Jason can represent real misunderstandings and are practically useful

Strategic Structural Reorganization in Multi-agent Systems Inspired by Social Organization Theory

Autonomic systems, capable of adaptive behavior, are envisioned as a solution for maintaining large, complex, real-time computing systems that are situated in dynamic and open environments. These systems are subject to uncertainties in their perceptual, computational, and communication loads. As a result, the individual system components find the need to cooperate with each other to acquire more information and accomplish complex tasks. Critical to the effective performance of these systems, is the effectiveness of communication and coordination methods. In many practical applications of distributed and multi-agent systems, the problem of communication and coordination becomes even more complicated because of the geographic disparity of tasks and/or agents that are performing the tasks. Experience with even small systems has shown that lack of an effective communication and coordination strategy leads the system to no-answer, or sub-optimal answer situations. To address this problem, many large-scale systems employ an additional layer of structuring, known as organizational structure, which governs assignment of roles to individual agents, existence of relations between the agents , and any authority structures in between. Applying different organizational structures to the same problem will lead to different performance characteristics. As the system and environment conditions change, it becomes important to reorganize to a more effective organization. Due to the costs associated with reorganization, finding a balance in how often or when a reorganization is performed becomes necessary. In multi-agent systems community, not a lot of attention has been paid to reorganizing a system to a different organizational structure. Most systems reorganize within the same structure, for example reorganizing in a hierarchy by changing the width or depth of the hierarchy. To approach this problem, we looked into adaptation of concepts and theories from social organization theory. In particular, we got insights from Schwaninger's model of Intelligent Human Organizations. We introduced a strategic reorganization model which enables the system to reorganize to a different type of organizational structure at run time. The proposed model employs different levels of organizational control for making organizational change decisions. We study the performance trade-offs and the efficacy of the proposed approach by running experiments using two instances of cooperative distributed problem solving applications. The results indicate that the proposed reorganization model results in performance improvements when task complexity increases