An Introduction to MultiAgent Systems.

The place of logic in Aristotle's thought In Metaph. E.1, Aristotle divides the sciences (=branches of knowledge) into three divisions: Theoretical (mathematics, natural science, theology), Practical (ethics, politics), and Productive (art, rhetoric). They are distinguished by their aims—truth, action, and production, respectively. Where is logic on this list? Aristotle does not seem to include it anywhere. This has been a subject of debate among subsequent interpreters. The question is whether logic is a subject matter to be studied (a science), or merely a method to be used by the various sciences. (This became a topic of dispute between the Stoics and Peripatetics.) The usual answer is that for Aristotle logic is not a subject matter, but a tool to be used by any science. That's why his collection of logical works is called the Organon— Greek for tool. (The title is due to Alexander of Aphrodisias, ca. 200 CE.) These are the works included in the Organon, and their topics: Categories terms De Interpretatione statements Prior Analytics theory of inference Posterior Analytics the axiomatic structure of a science Topics a manual of argumentation De Soph. Elench. a manual on fallacies The invention of logic It's fair to say that Aristotle invented deductive logic. (That's not to say that no one had drawn inferences before Aristotle told them how to do so. Rather, he was the first to codify inferences into a system, and to create rules for distinguishing correct from incorrect inferences.) Aristotle was justifiably proud of his creation. He even gives himself a pat on the back (SE, 183b34-184b7, Ackrill translation):

Aristotle's logic.

Intelligent agents are software components that can work autonomously and proactively to solve the problems collaboratively. To this end, they can behave in a cooperative manner and collaborate with other agents constituting systems called Multi-agent Systems (MAS). These systems have different perspectives such as the internal structure, plan, interaction, organisation, role, environment and so on. By having these views, MASs can consider the structure, behaviour, interaction, and environment of the complex systems such as Cyber-physical Systems (CPS). Therefore, intelligent software agents and MASs can be used in the modelling and development of CPSs. There are different Domain-specific Modelling Languages (DSMLs) to build MASs with a focus on various MAS aspects. One of the generative MAS DSMLs is SEA_ML++ which presents a thorough Model-driven Engineering practice with including the abstract syntax, graphical concrete syntax, model-to-model transformations and model-to-code transformations with the support of Platform Independent and Platform Specific levels of MAS modelling. In this chapter, we discuss how SEA_ML++ is used for the design and implementation of agent-based CPSs. An MDE methodology is introduced in which SEA_ML++ can be used to design agent-based CPS and implement these systems on various agent execution platforms. As the evaluating case study, the development of a multi-agent garbage collection CPS is taken into consideration. The conducted study demonstrates how this CPS can be designed according to the various viewpoints of SEA_ML++ and then implemented and executed on Jason platform.

Agent-based cyber-physical system development with SEA_ML++

This paper presents an extension of the agent-oriented domain-specific language ALAS to support Distributed Non-Axiomatic Reasoning. ALAS is intended for the development of specific kind of intelligent agents. It is designed to support the Siebog Multi-Agent System (MAS) and implementation of the Siebog intelligent agents. Siebog is a distributed MAS based on the modern web and enterprise standards. Siebog offers support to reasoning based on the Distributed Non-Axiomatic Reasoning System (DNARS). DNARS is a reasoning system based on the Non-Axiomatic Logic (NAL). So far, DNARS-enabled agents could be written only in Java programming language. To solve the problem of interoperability and agent mobility within Siebog platforms, the ALAS language has been developed. The goal of such language is to allow programmers to develop intelligent agents easier by using domain specific constructs. The conversion process of ALAS code to Java code is also described in this paper.

ALAS: agent-oriented domain-specific language for the development of intelligent distributed non-axiomatic reasoning agents

AgentDSM-Eval: A framework for the evaluation of domain-specific modeling languages for multi-agent systems

The agent technology represents one of the most consistent approaches to the development of distributed systems. Multiagent middleware XJAF, developed at the University of Novi Sad, presents a runtime environment that supports the execution of software agents. To solve the problem of interoperability, we propose a domain-specific agent language named ALAS, whose main purpose is to support the implementation and execution of agents on heterogenous platforms. To define the structure of the language, a metamodel and a grammar of the ALAS language has been created, in accordance with the requirements and needs of the agents. This paper describes the construction of the compiler and the generation of executable Java code that can be executed in XJAF.

Domain specific agent-oriented programming language based on the Xtext framework

Development of Agent-based languages is the natural extension of the research in the area of Agent-based systems. This paper deals with adding the support for the Distributed Non-Axiomatic Reasoning into the ALAS agent-oriented language. This support has been added into the Siebog agent middleware. Siebog is a distributed multiagent system based on the modern web and enterprise standards. Siebog has built in support for reasoning based on the Distributed Non-Axiomatic Reasoning System (DNARS). DNARS is a reasoning system based on nonaxiomatic logic (NAL) and general principles of development of non-axiomatic reasoning systems. So far, the DNARS-enabled agents could be written only in Java programming language. To solve the problem of interoperability within different Siebog platforms, an agentoriented domain-specific language (AODSL) ALAS has been developed. The main purpose of the ALAS is to support agent mobility and implementation and execution of agents on heterogenous platforms. This paper describes the extended version of the ALAS language which supports DNARS. The conversion process of ALAS code to Java code is also described in this paper. The latest version of ALAS has been developed by using textX framework and Arpeggio parser.

Extension of Agent-oriented Domain-specific language ALAS as a support to Distributed Non-Axiomatic Reasoning

This paper shows generation of JavaScript code from code written in agent-oriented domain-specific language ALAS. ALAS is an agent-oriented domain-specific language for writing software agents that are executed within XJAF middleware. Since the agents can be executed on various platforms, they must be converted into a language of the target platform. We also try to utilize existing tools and technologies to make the whole conversion process as simple as possible, as well as faster and more efficient. We use the Xtext framework that is compatible with Java to implement ALAS infrastructure - editor and code generator. Since Xtext supports Java, generation of Java code from ALAS code is straightforward. To generate a JavaScript code that will be executed within the target JavaScript XJAF implementation, Google Web Toolkit (GWT) is used.

Dejan Sredojević

Papers

ALAS: agent-oriented domain-specific language for the development of intelligent distributed non-axiomatic reasoning agents

Domain specific agent-oriented programming language based on the Xtext framework

Extension of Agent-oriented Domain-specific language ALAS as a support to Distributed Non-Axiomatic Reasoning

Conversion of the agent-oriented domain-specific language ALAS into JavaScript