Evaluation of KQML as an agent communication language

Mayfield, James; Labrou, Yannis; Finin, Tim

doi:10.1007/3540608052_77

Cited by 129 publications

(56 citation statements)

References 12 publications

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“…Strictly agents do not need to send messages formed with knowledge-level concepts but the assumption that they do is derived from the definition of agents as knowledge level entities. As a general design principle several researchers have commented on the desirability of high-level agent communication [35,112].…”

Section: Perception Subsumes Communication For Modelling Intention Rementioning

confidence: 99%

Modelling Intention Recognition for Intelligent Agent Systems

Heinze¹

2004

PsycEXTRA Dataset

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Mainstream visual psychology presents a 'sense then infer' account of vision that is analogous to the 'sense then infer' processing that characterises the agent intention recognition literature. From ecological psychology comes Gibson's theory of visual perception that highlights the importance of the environment in explaining the nature of vision and recognition and claims that higher order structures are directly accessible. This theory can be used as the stepping-off point for an account of intention recognition and the means by which it might be modelled. Furthermore, the capacity for virtual environments to be designed 'agent friendly' provides yet another dimension of design freedom. When accompanied by an explicit model of perception the intention recognition problem can be cast as a software design problem. The resulting design patterns provide useful options for modelling intention recognition in intelligent agent systems. When constructing military simulations that require sophisticated cognitive models there are plenty of challenges to occupy the developer. Human factors experts, particularly cognitivists, endeavour to gain an understanding of actual human psychology behind the behaviours to be modelled; computer scientists attempt to develop computational technologies with the attributes necessary to model those cognitive functions. Ultimately engineers must draw these models and technologies together in a manner that results in simulation systems that meet the expressed requirements. In the world of military simulation requirements vary substantively. High fidelity, whilst seemingly always desirable often conflicts with performance, maintainability, complexity, and other attributes that combine to make the software less useful. Balancing these requirements is aided by the availability of several architectures that allow the engineer the freedom to tradeoff various attributes of the system. This thesis presents a series of design patterns that provides software architectures useful for implementing intention recognition. Each of these architectures has a basis (although sometimes this tenuous) in psychology and cognitive modelling, and each imposes requirements on the technology necessary for implementation. The relative merits of the patterns are presented as are fully worked examples of their application to flight simulation. APPROVED FOR PUBLIC RELEASE in DSTO-RR-0286IV DSTO-RR-02M Author Clint Heinze Air Operations DivisionClint Heinze is a cognitive scientist interested in the research spaces where software engineering overlaps AI. Primarily this involves attempts to discover ways of engineering representations of intelligence that have the properties commonly desired of quality software -that is they should be robust, reliable, validated, etc. He has a degree in Aerospace Engineering from EMIT and has recently completed his PhD in the Department of Computer Science and Software Engineering at the University of Melbourne. Since 1989 the Defence Science and Technology Organisation have em...

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Section: Perception Subsumes Communication For Modelling Intention Rementioning

confidence: 99%

Modelling Intention Recognition for Intelligent Agent Systems

Heinze¹

2004

PsycEXTRA Dataset

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“…The receiver agent has no means to understand the content of a request and perform an action consequently, if the action to be performed is not exactly specified as the content of the message itself. This is clearly a strong limitation, which recent agent communication languages, such as KQML (Mayfield et al 1995) The same limitation affecting AGENT-0 also affects Concurrent METATEM: every agent has a communicative interface which the other agents in the system must know in order to exchange information. Despite this limitation, Concurrent METATEM has been proposed both as a coordination language (Kellett and Fisher 1997b) and as a language for forming groups of agents where agents have the ability to broadcast messages to the members of a group (Fisher and Kakoudakis 2000;Fisher 1998).…”

Section: Agent Knowledgementioning

confidence: 99%

Logic-based specification languages for intelligent software agents

Mascardi¹,

Martelli

Sterling

2004

Theory and Practice of Logic Programming

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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 E hhf . 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.

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“…We evaluated four such approaches using the same criteria as before (see Liu 1998 for details). The approaches we addressed include CORBA (the Common Object Request Broker Architecture, OMG 1997, von Bultzingsloewen et al 1996, Leppinen et al 1997, DCOM (the Distributed Component Object Model, Microsoft 1995Microsoft , 1996Microsoft , 1997, intelligent agent-based software engineering (Finin et al 1994, Genesereth and Ketchpel 1994, Mayfield et al 1996, and DIAS/DEEM (the Dynamic Information Architecture System/Dynamic Environmental Effects Model, Argonne National Laboratory 1995a, 1995b). These approaches encompass several different areas of computer science, including databases where the emphasis is on interoperation and data integration, software engineering where tool and environment integration issues dominate, artificial intelligence where systems consisting of distributed intelligent agents are being developed and explored, and information systems.…”

Section: Non-fem Approachesmentioning

confidence: 99%