ALAS: agent-oriented domain-specific language for the development of intelligent distributed non-axiomatic reasoning agents
TL;DR: An extension of the agent-oriented domain-specific language ALAS to support Distributed Non-Axiomatic Reasoning is presented, to allow programmers to develop intelligent agents easier by using domain specific constructs.
Abstract: 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.
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01 Jan 1891
TL;DR: The Organon as discussed by the authors is a collection of logical works written by the author of the first edition of the Organon, ca. 200 CE, and their topics are: 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
Abstract: 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):
61 citations
01 Jan 2021
TL;DR: In this article, an MDE methodology is introduced in which SEA_ML++ can be used to design agent-based Cyber-physical Systems (CPSs) and implement these systems on various agent execution platforms.
Abstract: 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.
12 citations
TL;DR: In this paper, an evaluation framework, called AgentDSM-Eval, with its supporting tool can be used to evaluate MAS DSMLs systematically according to various quantitative and qualitative aspects of agent software development.
8 citations
01 Feb 2021
TL;DR: A comparative MAS DSML evaluation methodology based on the Analytical Hierarchy Process is introduced and it showed that the agent developers prioritized appropriateness, completeness and shortening the development time as the most significant criteria for theMAS DSML assessment.
Abstract: Software agents and Multi-agent Systems (MAS) composed by these agents are used in the development of the complex intelligent systems. In order to facilitate MAS software development, various domain-specific modeling languages (DSMLs) exist. Unfortunately, the usability evaluation of these languages are mostly not considered or only a few assessments which cover one single MAS DSML are made. A comparative evaluation, which is missing in the existing studies, may help agent software developers to choose the MAS DSML which fits well into the system development requirements. Hence, in this paper, we introduce a comparative MAS DSML evaluation methodology based on the Analytical Hierarchy Process (AHP). A categorized set of MAS DSML criteria which can be used for the multi-criteria decision making is defined. These criteria can be prioritized by the developers according to their modeling language expectations and the application of the methodology allows the evaluation of DSML alternatives based on this prioritization. As the result of the automatic calculation of the importance distributions, the most appropriate DSML is determined. With the voluntarily participation of a group of agent software developers, the proposed methodology was applied for the comparative evaluation of four well-known MAS DSMLs. The conducted evaluation showed that the agent developers prioritized appropriateness, completeness and shortening the development time as the most significant criteria for the MAS DSML assessment while the attractiveness of the notations had a minimum effect on preferring a language. Favorite DSML for each comparison category and criteria was determined within this evaluation.
8 citations
References
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TL;DR: Agent theory is concerned with the question of what an agent is, and the use of mathematical formalisms for representing and reasoning about the properties of agents as discussed by the authors ; agent architectures can be thought of as software engineering models of agents; and agent languages are software systems for programming and experimenting with agents.
Abstract: The concept of an agent has become important in both Artificial Intelligence (AI) and mainstream computer science. Our aim in this paper is to point the reader at what we perceive to be the most important theoretical and practical issues associated with the design and construction of intelligent agents. For convenience, we divide these issues into three areas (though as the reader will see, the divisions are at times somewhat arbitrary). Agent theory is concerned with the question of what an agent is, and the use of mathematical formalisms for representing and reasoning about the properties of agents. Agent architectures can be thought of as software engineering models of agents;researchers in this area are primarily concerned with the problem of designing software or hardware systems that will satisfy the properties specified by agent theorists. Finally, agent languages are software systems for programming and experimenting with agents; these languages may embody principles proposed by theorists. The paper is not intended to serve as a tutorial introduction to all the issues mentioned; we hope instead simply to identify the most important issues, and point to work that elaborates on them. The article includes a short review of current and potential applications of agent technology.
6,714 citations
01 Jan 2003
3,093 citations
"ALAS: agent-oriented domain-specifi..." refers background in this paper
...Unlike other MAS based on the BDI architecture for the intelligent agents’ development (Bordini et al. 2009; Hindriks 2018; Wiebe van der and Wooldridge 2013; Pokahr et al. 2014; Weiss 2013), in the last upgrade Siebog has received a support for intelligent agents in the form of Distributed System for Non-Axiomatic Reasoning....
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...As mentioned above, there are numerous MASs intended for various domains....
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...MAS include an efficient agent messaging infrastructure and managing the agent’s life-cycle, provide support for agent mobility, to search for the capabilities of other agents, and so on....
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...The Siebog MAS in combination with the ALAS could greatly improve the management of e-commerce transaction networks because the Siebog is very convenient when it comes to a large number of participants in the system (in this case, sellers, buyers and products)....
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...In (Chau, Liu, and Lam 2009; Ip et al. 2010; Lam and Ip 2011) the following systems are proposed: MAS for six sigma projects selection process, MAS for Airport Service Planning and agent-based scheduling environment for Constraint Priority Scheduling respectively....
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TL;DR: The concept of agent-oriented programming is presented, the concept of mental state and its formal underpinning are discussed, a class of agent interpreters are defined, and a specific interpreter that has been implemented is described.
1,846 citations
"ALAS: agent-oriented domain-specifi..." refers background or methods in this paper
...The background and related work section describes couple of existing AOPLs and DSLs, technologies that had a strong influence on the development of ALAS as well as a brief overview of the Siebog agent middleware....
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...The emergence of agent technologies has led to the development of AOPLs....
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...In meanwhile, new versions of AGENT0 were created: PLACA (Thomas 1995) and Agent-K (Davies and Edwards 1994)....
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...Compared to GPLs, AOPLs are better suited for the design of agents, since they offer programming constructs for the agents design (for example, in AOPLs, message constructs are part of the language, not library calls)....
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...For the development of DSLs and AOPLs, developers use a variety of tools....
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Book•
01 Jul 2007
TL;DR: The Jason Agent Programming Language as discussed by the authors is a programming language based on the BDI Agent Model that allows to define simulated environments and communicate with multiple agents in a BDI agent language.
Abstract: Preface. 1 Introduction. 1.1 Autonomous Agents. 1.2 Characteristics of Agents. 1.3 Multi-Agent Systems. 1.4 Hello World! 2 The BDI Agent Model. 2.1 Agent-Oriented Programming. 2.2 Practical Reasoning. 2.3 A Computational Model of BDI Practical Reasoning. 2.4 The Procedural Reasoning System. 2.5 Agent Communication. 3 The Jason Agent Programming Language. 3.1 Beliefs. 3.2 Goals. 3.3 Plans. 3.4 Example: A Complete Agent Program. 3.5 Exercises. 4 Jason Interpreter. 4.1 The Reasoning Cycle. 4.2 Plan Failure. 4.3 Interpreter Configuration and Execution Modes. 4.4 Pre-Defined Plan Annotations. 4.5 Exercises. 5 Environments. 5.1 Support for Defining Simulated Environments. 5.2 Example: Running a System of Multiple Situated Agents. 5.3 Exercises. 6 Communication and Interaction. 6.1 Available Performatives. 6.2 Informal Semantics of Receiving Messages. 6.3 Example: Contract Net Protocol. 6.4 Exercises. 7 User-Defined Components. 7.1 Defining New Internal Actions. 7.2 Customising the Agent Class. 7.3 Customising the Overall Architecture. 7.4 Customising the Belief Base. 7.5 Pre-Processing Directives. 7.6 Exercises. 8 Advanced Goal-Based Programming. 8.1 BDI Programming. 8.2 Declarative (Achievement) Goal Patterns. 8.3 Commitment Strategy Patterns. 8.4 Other Useful Patterns. 8.5 Pre-Processing Directives for Plan Patterns. 9 Case Studies. 9.1 Case Study I: Gold Miners. 9.2 Case Study II: Electronic Bookstore. 10 Formal Semantics. 10.1 Semantic Rules. 10.2 Semantics of Message Exchange in a Multi-Agent System. 10.3 Semantic Rules for Receiving Messages. 10.4 Semantics of the BDI Modalities for AgentSpeak. 11 Conclusions. 11.1 Jason and Agent-Oriented Programming. 11.2 Ongoing Work and Related Research. 11.3 General Advice on Programming Style and Practice. A Reference Guide. A.1 EBNF for the Agent Language. A.2 EBNF for the Multi-Agent Systems Language. A.3 Standard Internal Actions. A.4 Pre-Defined Annotations. A.5 Pre-Processing Directives. A.6 Interpreter Configuration. Bibliography.
1,173 citations
01 Feb 1996
TL;DR: This paper provides an alternative formalization of BDI agents by providing an operational and proof-theoretic semantics of a language AgentSpeak(L), which can be viewed as an abstraction of one of the implemented BDI systems and allows agent programs to be written and interpreted in a manner similar to that of horn-clause logic programs.
Abstract: Belief-Desire-Intention (BDI) agents have been investigated by many researchers from both a theoretical specification perspective and a practical design perspective. However, there still remains a large gap between theory and practice. The main reason for this has been the complexity of theorem-proving or model-checking in these expressive specification logics. Hence, the implemented BDI systems have tended to use the three major attitudes as data structures, rather than as modal operators. In this paper, we provide an alternative formalization of BDI agents by providing an operational and proof-theoretic semantics of a language AgentSpeak(L). This language can be viewed as an abstraction of one of the implemented BDI systems (i.e., PRS) and allows agent programs to be written and interpreted in a manner similar to that of horn-clause logic programs. We show how to perform derivations in this logic using a simple example. These derivations can then be used to prove the properties satisfied by BDI agents.
1,142 citations
"ALAS: agent-oriented domain-specifi..." refers background in this paper
...One of the first AOP languages was AgentSpeak (Rao 1996)....
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