AGENT TECHNOLOGY ROADMAP

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Executive Summary

An agent is a computer system capable of flexible autonomous action in a dynamic, unpredictable and open environment. Agent technologies are a natural extension of current component-based approaches, and have the potential to greatly impact the lives and work of all of us and, accordingly, this area is one of the most dynamic and exciting in computer science today. Some application domains where agent technologies will play a crucial role include: Ambient Intelligence, the seamless delivery of ubiquitous computing, continuous communications and intelligent user interfaces to consumer and industrial devices; Grid Computing, where multi-agent system approaches will enable efficient use of the resources of high-performance computing infrastructure in science, engineering, medical and commercial applications; Electronic Business, where agent-based approaches are already supporting the automation and semi-automation of information-gathering activities and purchase transactions over the Internet; the Semantic Web, where agents are needed both to provide services, and to make best use of the resources available, often in cooperation with others; Bioinformatics and Computational Biology, where intelligent agents may support the coherent exploitation of the data revolution occurring in biology; and others including monitoring and control, resource management, and space, military and manufacturing applications, for example.

The impact of agent technologies in application domains such as these will occur in a number of ways: firstly, as a metaphor for the design of complex, distributed computational systems; secondly, as a source of technologies for such computing systems, and thirdly, as models of complex real-world systems, such as those found in biology and economics. This report examines these various impacts and presents a status report of current research and application developments for each of the major technologies involved in multi-agent systems.

As the computing landscape moves from a focus on the individual standalone computer system to a situation in which the real power of computers is realised through distributed, open and dynamic systems, we are faced with new technological challenges and new opportunities. The characteristics of dynamic and open environments in which, for example, heterogeneous systems must interact, span organisational boundaries, and operate effectively within rapidly changing circumstances and with dramatically increasing quantities of available information, suggest that improvements on the traditional computing models and paradigms are required. In particular, the need for some degree of autonomy, to enable components to respond dynamically to changing circumstances while trying to achieve over-arching objectives, is seen by many as fundamental. In practical developments, Web Services, for example, now offer fundamentally new ways of doing business through a set of standardised tools, and support a service-oriented view of distinct and independent software components interacting to provide valuable functionality. In the context of such developments, agent technologies have become some of the primary weapons in the arsenal aimed at addressing the emergent problems, and managing the complexity.

Agents as Design
The use of agents as an abstraction tool, or a metaphor, for the design and construction of systems provided the initial impetus for developments in the field. On the one hand, agents offered an appropriate way to consider complex systems with multiple distinct and independent components. On the other, they also enable the aggregation of different functionalities that have previously been distinct (such as planning, learning, coordination, etc) in a conceptually embodied and situated whole. Thus these notions provide a set of technology areas that relate directly to these abstractions in the design and development of large systems, of individual agents, of ways in which agents may interact to support these concepts, and in the consideration of societal or macro-level issues such as organisations and their computational counterparts. Current efforts span diverse areas including agent-oriented software engineering; agent architectures; mobile agent systems; agent infrastructure; electronic institutions.

Agent technologies
Agent-based approaches have been a source of technologies to a number of research areas, both theoretical and applied. These include distributed planning and decision-making, automated auction mechanisms, communication languages, coordination mechanisms, matchmaking architectures and algorithms, ontologies and information agents, negotiation, and learning mechanisms. Moreover, agent technologies have drawn from, and contributed to, a diverse range of academic disciplines, in the humanities, the sciences and the social sciences.

The Agent Technology Roadmap
Based on this examination of current status, we envision four major phases of agent research and development over the next decade. Current deployments typically centre on closed agent systems with ad-hoc designs, predefined communications protocols and scalability only in simulations. Note that this can have a negative impact on interoperation with external non-agent legacy systems (currently these comprise the vast majority of existing computer systems), and on the question of how systems and solutions can migrate to incorporating agent-based concepts. If agents are to succeed, we cannot afford to start from scratch, but need to show how existing solutions and systems can migrate towards agent-based systems and solutions. The second phase, covering the period to 2005 or so, will increasingly see the use of semi-structured agent communications languages (such as FIPA ACL), top-down design methodologies such as GAIA, and scalability extended to predetermined and domain-specific environments. The third phase, covering approximately 2006-2008, will see the use of agreed protocols and languages, and of standard, agent-specific design methodologies in open agent systems in specific domains (such as those in bioinformatics and eCommerce). More general scalability, to include arbitrary numbers and diversity of agents, in each such domain will likely be achieved by this phase, with bridging agents translating between domains. The final stage, from 2009 or so onwards, will see truly-open and fully-scalable multi-agent systems, across domains, with agents capable of learning appropriate communications protocols upon entry to a system, and with protocols emerging and evolving through actual agent interactions. This bears strong similarities to IBM's ongoing research project on autonomic computing and existing Semantic Web objectives.

Technological Challenges
Arising from this picture of the future of agent research, we see a number of broad technological challenges for research and development over the next decade.

• Increase quality of agent software to industrial standard. One of the most fundamental obstacles to large-scale take-up of agent technology is the lack of mature software development methodologies for agent-based systems. Clearly, basic principles of software and knowledge engineering need to be applied to the development and deployment of multi-agent systems, but they also need to be augmented to suit the differing demands of this new paradigm.
• Provide effective agreed standards to allow open systems development. In addition to standard languages and interaction protocols, open agent societies will require the ability to collectively evolve languages and protocols specific to the application domain and to the agents involved. Some work has commenced on defining the minimum requirements for a group of agents with no prior experience of each other to evolve a sophisticated communications language, but this work is still in its infancy. Research in this area will draw on linguistics, social anthropology, biology, the philosophy of language and information theory.
• Provide semantic infrastructure for open agent communities. At present, information agents exist in academic and commercial laboratories, but are not widely available in real world applications. The move out of the laboratory is likely to happen in the next ten years, but requires: a greater understanding of how agents, databases and information systems interact; investigation of the real-world implications of information agents (for example, including the economic effects of shopbots); and development of benchmarks for system performance and efficiency. In order to support this, further needs include: new web standards that enable structural and semantic description of information; and services that make use of these semantic representations for information access at a higher level. The creation of common ontologies, thesauri or knowledge bases play a central role here, and merits further work on the formal descriptions of information and, potentially, a reference architecture to support the higher level services mentioned above.
• Develop reasoning capabilities for agents in open environments. At present, organisational approaches do not adequately handle the issues inherent in open multi-agent systems, namely heterogeneity of agents, trust and accountability, failure handling and recovery, and societal change. The next challenge for agent-based computing is to develop appropriate representations of analogous computational concepts to the norms, legislation, authorities, enforcement, etc., that can underpin the development and deployment of dynamic electronic institutions. Similarly, virtual organisations involve dynamic coalitions of small groups that can provide more services and make more profits than an individual group. Moreover, such coalitions can disband when they are no longer effective. At present, coalition formation for virtual organisations is limited, with such organisations largely static. The automation of coalition formation will save both time and labour, and may be more effective at finding better coalitions than humans in complex settings. Related issues include negotiation and argumentation, and domain-specific models of reasoning, both of which may be used to form such groups of agents in open environments.
• Develop agent ability to understand user requirements. At the architecture level, future avenues for learning research include developing distributed models of profile management, as well as more general distributed agent learning techniques rather than just single agent learning in multi-agent domains. Developing approaches to personalisation that can operate in a standards-based, pervasive computing environment presents many interesting research challenges including, how to integrate machine learning techniques (for profile adaptation) with structured XML-based profile representations. Another area deserving of greater activity is that of distributed profile management - a task for which the agent-based paradigm should be well suited. The impact of the emerging Semantic Web on approaches for wrapper induction and text-mining also requires careful study
• Develop agent ability to adapt to changes in environment. Even though learning technology is clearly crucial for open and scalable multi-agent systems, it is still in early development. While there has progress in many areas, such as evolutionary approaches and reinforcement learning, these have still not made the transition to real-world applications. Reasons for this can be found in problems of scalability and in user trust in self-adapting software. In the longer term, learning techniques are likely to become a central part of agent systems, while the shorter term offers application opportunities in areas such as interactive entertainment, which are not safety-critical.
• Ensure user confidence and trust in agents. Collaboration of any kind, especially in situations in which computers act on behalf of users or organisations, will only succeed if there is trust. For this trust to be given requires a variety of factors to be in place. First, a user must have confidence that an agent or group of agents which represents them within an open system will act effectively on their behalf - it must be at least as effective as the user would be in similar circumstances. Second, agents must be secure and tamper-proof, and must not reveal information inappropriately (e.g., bank account details). There is much work on system security, cryptography and privacy which can be exploited and adapted for use in agent technology. Finally, if a user is to trust the outcome of an open agent system, they must have confidence that agents representing other parties or organisations will behave within certain constraints. Mechanisms to do this include: reputation mechanisms; the use of norms (social rules) by all members of an open system; self-enforcing protocols, which ensure that it is not in the interests of any party to break them; and electronic contracts.

Challenges for the European Agent Community
Achieving this vision will require considerable investment in research and development in a number of areas of applied and pure research, and in commercial deployment and implementation. Europe begins from a strong position, with world-class expertise in formal and logical models of agent systems, in argumentation, in standardisation activity (particularly FIPA) and in particular agent application areas, such as the management of utility networks and bioinformatics. In addition, Europe has a solid base in many other areas such as organisations, social systems and communication semantics. Relative to the USA and Japan, Europe's weaknesses are in the areas of economic auction mechanisms, and in applications to military, games and robotics domains. To achieve the full potential of agent approaches and technologies from Europe's current position, we recommend that research and development resources be focused along several key directions, as follows.

• Leveraging underpinning work in related areas of Computer Science, such as software engineering, distributed computing and object technologies. This is likely to become increasingly important as agent technologies converge with other approaches, such as Web Services, semantic web technologies, P2P computing, and so on.
• Strengthening links with other areas of Computer Science working on different problems, such as the communities active in artificial life, traditional mathematical modelling & simulation, semantic web activities, pervasive computing and uncertainty in AI.
• Strengthening links with other disciplines, particularly Economics, Management Sciences, Marketing, Logic, Philosophy, Biology, Sociology and Political Science.
• Encouraging industry adoption and commercial development of agent technologies, by identifying the obstacles to take-up and developing appropriate training and support mechanisms, software tools, prototypes, and case studies to overcome these obstacles. Finding a killer application to get the concept of agents across will be a key driver. Early signs indicate that online advice, supply chain management and human capital management might prove to be catalysts.
• Assessment of non-functional requirements such as dependable agent systems addressing issues including trustworthiness and dynamics of adaptable complex systems.
• Relating agent standards, tools and concepts to industrially accepted standards for development and middleware.

This roadmap was developed by the AgentLink Network of Excellence for Agent-Based Computing. The exercise involved extensive discussions throughout the community, both European and international. The roadmap is endorsed by Acklin, The Netherlands; Aegis, France; Agents Inspired Technologies, Spain; Almende, The Netherlands; Applied Intelligence, UK; Computas, Norway; Emorphia, UK; FIPA; Labein, Spain; Lost Wax, UK; ThinkinGolem, Italy; Whitestein Technologies, Switzerland; and Wittman and Partners Computer Systems, Romania. Additionally, the roadmap activity is supported by Agentcities; Coco Software Engineering, Austria; Hewlett Packard Laboratories, UK; Magenta Corporation, UK; Motorola, France; Siemens, Germany; Tryllian, The Netherlands; and Telefonica, Spain. ;