Agent Based Software Development

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Agent Based Software Development

• Michael Luck, Ronald Ashri and Mark dInverno

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1 Agent Based Computing

• Modern computing is defined by the
  interconnection of computers
• The World Wide Web now provides a basic
  infrastructure for dynamic provision of online
  services
• The information society arises from the use of
  distributed, dynamic and open systems across all
  aspects of everyday life
• Computers are in embedded devices, support
  systems as well as the traditional desktop

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Dynamic and open environments

• Networked computers
• Dynamic interactions to form new system
  configurations
• Enable increased flexibility
• May span organisational boundaries
• Can operate
• in rapidly changing circumstances
• with changing information
• Traditional models of computation are inadequate

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Autonomy

• Need
• to respond dynamically to changing circumstances
• while trying to achieve overarching objectives
• without user intervention
• Web Services, for example
• offer new ways of operating through standardized
  tools
• support a service-oriented view of software
• Agent technologies offer a way to tackle the
  problems and manage the resulting complexity

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Agents

• Agents can be viewed as autonomous,
  problem-solving computational entities capable of
  effective operation in dynamic and open
  environments.
• They are often deployed in environments in which
  they interact, and possibly cooperate, with other
  agents (including both people and software) that
  may have conflicting aims.

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Agent-Based Systems

• Agent-based systems have emerged from a
  convergence of technologies in
• Distributed object systems
• Distributed artificial intelligence
• Agent technologies are already providing benefits
  in, for example,
• Manufacturing
• Supply chain management
• B2B exchanges

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Agents versus Objects

• Like objects, agents encapsulate state and
  behaviour
• But objects are essentially passive
• They have no choice over interaction
• They are invoked by other objects
• Agents decide whether to perform the desired
  operation
• Agents are autonomous

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Agents versus Objects Modeling

• Objects provide a valuable way to model the world
• But agents provide a more natural representation
  of real-world systems in which different
  individuals interact according to their own
  agendas and priorities.
• They can come together to achieve overarching
  objectives that might not, or not as easily, be
  achieved by the individuals alone

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Agents versus Objects

• When agent goals are closely aligned, and if
  agents are benevolent and honest, then the
  resulting systems may resemble an object-oriented
  system.
• The object-oriented paradigm may be adequate, but
  is unlikely to be flexible.
• Agents may be constructed using object
  technology.
• Agents typically run in their own thread of
  control, while standard object systems have one
  thread.

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Agents basic notions

• Agents are
• Situated
• Embodied
• Agents
• receive input through some sensory device
• act so as to affect the environment through
  effectors

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Agents definitions

• There is no consensus over what constitutes an
  agent, but some properties are agreed
• Autonomy self-starting, independent entities,
  that can function without direct user or
  programmer intervention
• Reactiveness can monitor environment and respond
  quickly to changes
• Proactiveness have overarching goals that guide
  behaviour over longer periods
• Social ability open environments require the
  ability to interact and communicate with other
  agents

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Other Agent Properties

• Learning ability
• Mobility
• Benevolence
• Rationality
• Temporal continuity
• Believable personality
• communication ability
• Adaptability
• Mobility
• Veracity

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Weak and Strong Notions

• Weak notion involves
• Autonomy
• Social ability
• Reactiveness
• Proactiveness
• Strong or intentional notion of agents also
  requires control architectures comprising mental
  components such as
• Beliefs
• Desires
• Motivations

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Definitions

• Franklin and Graesser autonomous agent is a
  system situated within and a part of an
  environment that senses that environment and acts
  on it, over time, in pursuit of its own agenda
  and so as to affect what it sense in the future.

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Types and Applications

• Generic autonomous agents, software agents,
  intelligent agents
• Specific interface agents, virtual agents,
  information agents, mobile agents
• Applications operating systems interfaces,
  processing satellite imaging data, electricity
  distribution management, air-traffic control,
  business process management, electronic commerce,
  computer games.

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History

• Rooted in
• Distributed artificial intelligence
• Distributed object technologies
• Basic notions stem from the work of Brooks
• Objected to traditional view of AI as symbol
  manipulation
• Argued for construction of situated and embodied
  systems.
• Wanted to address brittleness of existing systems
  
• Sought more flexible and robust complete systems
  that exhibited effective behavior in changing
  environments.

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Distributed Artificial Intelligence

• Dates back to mid to late 1970s
• Concerned with development of mechanisms to
  enable systems of interacting agents to pursue
  overarching goals in a cooperative fashion.
• Gave rise to the Functionally-Accurate,
  Cooperative (FA/C) paradigm
• Provided a model for task decomposition
• Enabled agent interaction in a distributed
  problem-solving system
• Agents no longer needed to have all information
  locally to solve subproblems
• Worked through synchronous exchange of partial
  results
• Evolved into much of field of multi-agent systems

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Distributed Object Technologies

• Provided a supporting infrastructure
• For example, the CORBA distributed computing
  platform handled low level interoperation of
  heterogeneous distributed components
• CORBA can underpin the development of agent
  systems without the need for reinvention of
  fundamental techniques.

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World Wide Web

• Distribution of information and associated
  technologies lend themselves almost ideally for
  multi-agent systems
• Problems that arise suggest agents
• The dual aspect of this interaction with the
  World Wide Web has thus been a major driving
  force

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Obstacles to Deployment

• Fundamental obstacle to take-up lack of mature
  agent development methodologies
• Requires
• Basic principles of software and knowledge
  engineering augmented to suit the differing
  demands of agents
• sophisticated yet easy-to-use agent-oriented CASE
  environments for all aspects of system
  development process
• Some systems already have rudimentary elements of
  these
• Success is likely through use of evolving (and
  current) systems integration technologies (such
  as Jini and UDDI)

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Ambient Intelligence

• Aimed at seamless delivery of services and
  applications
• Relies on
• ubiquitous computing,
• ubiquitous communication
• intelligent user interfaces
• Environment of potentially thousands of embedded
  and mobile devices interacting to support user
  centered goals and activity.
• Component-oriented view of the world
• Key characterizing features are autonomy,
  distribution, adaptation, responsiveness, etc.

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Grid Computing

• High performance computing infrastructure for
• large scale distributed scientific endeavor
• more general applications involving large scale
  information handling, knowledge management and
  service provision
• Many services
• spread over a geographically distributed
  environment
• new services join and existing ones leave
• Agents
• act for service owners, managing access to
  services, ensuring that contracts are fulfilled
• act for service consumers, locating services,
  agreeing contracts, and receiving and presenting
  results
• collaborate and form coalitions with different
  capabilities in support of new virtual
  organisations.

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eBusiness

• Agents already used in product and merchant
  discovery and brokering
• Next step is real trading, negotiating deals and
  making purchases
• Major impact will be on the supply chain
• Direct consumer contact with producer instead of
  reseller may produce increase in efficiency of
  overall supply chain
• Will permit new markets to appear, old markets to
  change and the participation of new players.
• In short term, travel agencies and retailing will
  be the primary B2C domains using agent technology

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Simulation

• Natural basis for training of decision makers in
  complex domains
• Defense simulations enable planners to experience
  complex military operations and war games
• Actual market dynamics can be simulated to give
  trainee decision makers exposure to many diverse
  experiences
• Decision maker is allowed to learn through
  mistakes, without real-world consequences
• Entertainment applications include
• Single (human) player computer games
• Multi-player games, where players may be both
  humans and agents
• Interactive movies and television