Benjamin Hirsch

Merging Agents and Services — the JIAC Agent Platform

The JIAC V serviceware frame work is a Java based agent framework with its emphasis on industrial requirements such as software standards, security, management, and scalability. It has been developed within industry- and government-funded projects during the last two years. JIAC combines agent technology with a service oriented approach. In this chapter we describe the main features of the framework, with a particular focus on the language JADL++ and the service matching capabilities of JIAC V.

COMPUTATIONAL LOGICS AND AGENTS: A ROAD MAP OF CURRENT TECHNOLOGIES AND FUTURE TRENDS

The concept of an agent is increasingly used in contemporary software applications, particularly those involving the Internet, autonomous systems, or cooperation. However, with dependability and safety in mind, it is vital that the mechanisms for representing and implementing agents are clear and consistent. Hence there has been a strong research effort directed at using formal logic as the basis for agent descriptions and agent implementation. Such a logical basis not only presents the clarity and consistency required but also allows for important techniques such as logical verification to be applied. We present a road map of research into the use of computational logic in agent‐based systems and survey much of the recent work in these areas. Even though, with such a rapidly changing field, it is impossible to cover every development, we aim to give the reader sufficient background to understand the current research problems and potential future developments in this maturing area.

A novel multi-agent system utilizing quantum-inspired evolution for demand side management in the future smart grid

The Smart Grid has become the future choice by many utility departments to attain bottom line goals of energy management. The Smart Grid will depend on a large number of renewable energy resources which require sophisticated control and coordination mechanisms for efficient and reliable demand side management DSM. In this paper, we propose a multi-agent based system to control and coordinate the operation among different entities within the Smart Grid. Specifically, the agents autonomously coordinate their activities to satisfy the local constraints of different entities while at the same time satisfying the underlying global goal of energy management. The novelty of our system is in formulating the coordination problem among the agents as a multi-objective optimization problem solved by a quantum-inspired evolution algorithm. We have extensively evaluated the system using the JIAC-V multi-agent platform. Experimental results show that our system is feasible and effective. Our method of coordinating the energy consumption of consumers using controller agents provides the basis for energy management at peak times. Thus, it promotes the wide application of the proposed system.

Towards a mapping from BPMN to agents

In industry, people who design business processes are often different from those designing the technical realization. Also, they generally use different languages, such as BPMN on the one hand and UML on the other. While agents are theoretically suitable for designing and implementing business ideas, multi-agent methodologies are generally not geared towards them. In this paper, we describe the first step of mapping business process diagrams to agent concepts. To this end, we present a graph based representation of BPMN together with structural and semantical analysis methods. These provide the necessary formal grounding for the mapping we have in mind.

JADL – an agent description language for smart agents

In this paper, we describe the declarative agent programming language Jadl (JIAC Agent Description Language). Based on three-valued logic, it incorporates ontologies, FIPA-based speech acts, a (procedural) scripting part for (complex) actions, and allows to define protocols and service based communication. Rather than relying on a library of plans, the framework implementing jadl allows agents to plan from first principles. We also describe the framework and some applications that have been implemented.

Organising Logic-Based Agents

In this paper we address the task of organising multi-agent systems in order to collectively solve problems. We base our approach on a logical model of rational agency comprising a few simple, but powerful, concepts. While many other researchers have tackled this problem using formal logic, the important aspect of the work described here is that the logical descriptions of the agents are directly executable using the Concurrent METATEM framework, allowing the execution of agents described in a combination of temporal, belief and ability logics. Here, we are particularly concerned with exploring some of the possible logical constraints that may be imposed upon these agents, and how these constraints affect the ability of the agents to come together to collectively solve problems.

Programming groups of rational agents

In this paper, we consider the problem of effectively programming groups of agents. These groups should capture structuring mechanisms common in multi-agent systems, such as teams, cooperative groups, and organisations. Not only should individual agents be dynamic and evolving, but the groups in which the agents occur must be open, flexible and capable of similar evolution and restructuring. We enable the description and implementation of such groups by providing an extension to our previous work on programming languages for agent-based systems based on executable temporal and modal logics. With such formalism as a basis, we consider the grouping aspects within multi-agent systems. In particular, we describe how this logic-based approach to grouping has been implemented in Java and consider how this language can be used for developing multi-agent systems.

Collaborative learning in action

In this paper we introduce the Collaborative Learning Environment (CLE), a novel document creation tool, designed at the Etisalat-British Telecomm Innovation Center (EBTIC) and created to enable and enhance team-based negotiation of online, interactive writing tasks. We provide the context in which the tool has been trialed, and discuss a case study where the tool is used to gain a deep understanding into the way students thought about and implemented given tasks.

Quantitative approach to collaborative learning: performance prediction, individual assessment, and group composition

The benefits of collaborative learning, although widely reported, lack the quantitative rigor and detailed insight into the dynamics of interactions within the group, while individual contributions and their impacts on group members and their collaborative work remain hidden behind joint group assessment. To bridge this gap we intend to address three important aspects of collaborative learning focused on quantitative evaluation and prediction of group performance. First, we use machine learning techniques to predict group performance based on the data of member interactions and thereby identify whether, and to what extent, the group’s performance is driven by specific patterns of learning and interaction. Specifically, we explore the application of Extreme Learning Machine and Classification and Regression Trees to assess the predictability of group academic performance from live interaction data. Second, we propose a comparative model to unscramble individual student performances within the group. These performances are then used further in a generative mixture model of group grading as an explicit combination of isolated individual student grade expectations and compared against the actual group performances to define what we coined as collaboration synergy - directly measuring the improvements of collaborative learning. Finally the impact of group composition of gender and skills on learning performance and collaboration synergy is evaluated. The analysis indicates a high level of predictability of group performance based solely on the style and mechanics of collaboration and quantitatively supports the claim that heterogeneous groups with the diversity of skills and genders benefit more from collaborative learning than homogeneous groups.

SeMa 2 :A Hybrid Semantic Service Matching Approach

The SeMa2 software module is a hybrid semantic service matchmaker based on OWL-S service descriptions and SWRL rules that participated in the S3 Contests 2008–2010. It provides syntactical as well as semantical matching techniques. Besides classifying input and output parameters it offers an approach to precondition and effect rule matching and reasoning. In this chapter we describe the architecture and the workflow of our approach and the results that have been evaluated during the last S3 Contest and with experimental test runs. Finally, we discuss the benefits and drawbacks of our implementation and propose future steps on our work.