Agent oriented programming: An overview of the framework and summary of recent research

This is a short overview of the agent-oriented programming (AOP) framework. AOP can be viewed as an specialization of object-oriented programming. The state of an agent consists of components called beliefs, choices, capabilities, commitments, and possibly others; for this reason the state of an agent is called its mental state. The mental state of agents is captured formally in an extension of standard epistemic logics: beside temporalizing the knowledge and belief operators, AOP introduces operators for commitment, choice and capability. Agents are controlled by agent programs, which include primitives for communicating with other agents. In the spirit of speech-act theory, each communication primitive is of a certain type: informing, requesting, offering, etc. This document describes these features in more detail and summarizes recent results and ongoing AOP-related work

Agent-oriented Programming in Defence Domain

Research in distributed artificial intelligence has given rise to agent-oriented programming (AOP), an advanced software modelling paradigm. It has several benefits when compared with the existing development approaches, in particular, the ability to let agents represent high-level abstractions of active entities in a software system. Although still young and under evolution, this paradigm has already shown particular promise in a number of areas. This paper gives an overview of this paradigm, its benefits over the other conventional programming paradigms being used. It also proposes the decision support system model for the military domain.In the proposed system there are certain critical issues, which need to be focused upon. The existing conventional paradigms are inadequate to deal with these issues. This paper identifies these critical issues and discusses how AOP can address these issues

Logic-Based Specification Languages for Intelligent Software Agents

The research field of Agent-Oriented Software Engineering (AOSE) aims to find abstractions, languages, methodologies and toolkits for modeling, verifying, validating and prototyping complex applications conceptualized as Multiagent Systems (MASs). A very lively research sub-field studies how formal methods can be used for AOSE. This paper presents a detailed survey of six logic-based executable agent specification languages that have been chosen for their potential to be integrated in our ARPEGGIO project, an open framework for specifying and prototyping a MAS. The six languages are ConGoLog, Agent-0, the IMPACT agent programming language, DyLog, Concurrent METATEM and Ehhf. For each executable language, the logic foundations are described and an example of use is shown. A comparison of the six languages and a survey of similar approaches complete the paper, together with considerations of the advantages of using logic-based languages in MAS modeling and prototyping.Comment: 67 pages, 1 table, 1 figure. Accepted for publication by the Journal "Theory and Practice of Logic Programming", volume 4, Maurice Bruynooghe Editor-in-Chie

Agent programming in the cognitive era

It is claimed that, in the nascent ‘Cognitive Era’, intelligent systems will be trained using machine learning techniques rather than programmed by software developers. A contrary point of view argues that machine learning has limitations, and, taken in isolation, cannot form the basis of autonomous systems capable of intelligent behaviour in complex environments. In this paper, we explore the contributions that agent-oriented programming can make to the development of future intelligent systems. We briefly review the state of the art in agent programming, focussing particularly on BDI-based agent programming languages, and discuss previous work on integrating AI techniques (including machine learning) in agent-oriented programming. We argue that the unique strengths of BDI agent languages provide an ideal framework for integrating the wide range of AI capabilities necessary for progress towards the next-generation of intelligent systems. We identify a range of possible approaches to integrating AI into a BDI agent architecture. Some of these approaches, e.g., ‘AI as a service’, exploit immediate synergies between rapidly maturing AI techniques and agent programming, while others, e.g., ‘AI embedded into agents’ raise more fundamental research questions, and we sketch a programme of research directed towards identifying the most appropriate ways of integrating AI capabilities into agent programs

An animated metaphor for agent oriented programming

The term Animated Systems has been introduced in the bibliography in reference to interactive dynarnic worlds simulations, composed of interacting independent objects [Tra96]. Simulation is a powerful tool because it allows the construction of virtual worlds that model a part of the real world. The laws of physics, the animal behavior patterns, are no longer abstract theories, and they transform into tangible realities. Through the creation, the observation and the modification of the virtual world it is possible to obtain an enhanced comprehension of the world that is being modeled. The most flexible way to create a simulation is by programming it [Cyp95]. The environments and languages of conventional programming allow the development of virtual worlds, but they are not adequate for this task. The conception of a program as a sequence of instructions, on what the procedural model is based, requires a considerable capacity for mental contortion. Even object oriented prograrnming, based on message passing, demands a strong level of abstraction. In particular, they are too complex for novice users. We cannot eliminate the inherent complexity of the problem of building a virtual world, but we can search for tools that are expressive enough so the task is not complicated any further. So, the construction of dynamic worlds requires paradigms, environments and prograrnming languages that provide a new way of thinking about programs [Cyp94]. This article proposes agent based prograrnming as a metaphor for building worlds of interactive autonomous objects. This alternative is attractive because it is natural to build animated systems on the base of a metaphor that takes elements of live agents of the real world to build a virtual world.Eje: Aspectos teóricos de la inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

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