Agent Based Software Development

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Agent Based Software Development

• Michael Luck, Ronald Ashri and Mark dInverno

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Agent Architectures

• What kind of computational entity is an agent?
• Why architecture?
• to predict what an agent in its current state
  will do in its current environment
• a methodology or blueprint for building agent
  systems

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Different Agent Architectures

• Single-agent (micro)
• reactive agent architectures
• deliberative agent architectures
• Hybrid agent architectures
• Multi-agent (micro and macro)
• Macro norms, protocols, languages
• Micro designing individual agents that can work
  effectively in a society

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From the Traditional AI View to Reactive Agent
Architectures

• Traditional AI View
• Symbolic (internal) representation
• Manipulate this representation to work out what
  to do next
• More recent View
• Effective behaviour does not need representation
  and manipulation
• Symbolic reasoning is very resource demanding
• Agents must be situated and embodied with symbol
  reflex rules

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Reactive Agent Architectures

• behaviour directly coupled with the world
• stimulus-response rules
• Brooks was a pioneer
• Subsumption Architecture
• Maes also
• Agent network architecture
• Intelligent behaviour is an emergent phenomenon
  arisiing from the interaction of societies of
  nonintelligent systems

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Subsumption Architectures

• A means of controlling behaviour of mobiles
  robots in real time 3 basic requirements
• Agents that can cope with multiple goals
• Agents should have multiple sensors
• Agents should be robust
• Function when some sensors fail
• Function in dynamic uncertain environments

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Layered Architecture Task Achieving Behaviours

• Avoid contact with objects
• Wander
• Explore
• Build a map and plan routes
• Notice environment change
• Identify objects, and perform certain
  object-related tasks
• Build and execute plans to change world in some
  desirable way
• Reason about the behaviour of other objects, and
  modify plans

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Subsumption Architecture Operation

• Layers unaware of layers above them
• Layers able to examine details of layers below
  them
• Each layer connected to all sensors but only
  extracts information relevant to that layer
• Layers can suppress lower layers
• suppressing inputs
• inhibiting outputs

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The Subsumption Architecture
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Pros and Cons of the Subsumption Architecture

• Pros
• Showed decomposition by behaviour rather than
  function was possible
• Modular structure and a clear control
  methdolology
• Impact on other architectures
• Cons
• No explicit reasoning how would it scale up?
• No design methodology built on intuition rather
  than a set of guiding principles
• Difficult to perform any formal analysis and so
  predict or explain behaviour

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Agent Network Architecture

• Based on the notion that intelligent systems
  consist of societies of mindless interacting
  systems
• Consists of competence modules
• compete for behaviour using activation
• Activation based on
• whether a module is currently executable
• perception of the environment
• current goals

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Agent Network Architecture Operation

• Modules are linked
• activated modules increase the activation of
  their successors
• Non executable models activate their predecessors
• All modules decrease activation of their
  conflictors
• Architecture is distributed, modular and robust.
• Has not been extended for designing multi-agent
  systems in general

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Deliberative Agent Architectures

• Maintain and manipulate representations of the
  world
• To model rational or intentional agency use
  mental attitudes
• Eg. Beliefs, goals, desires, knowledge, plans,
  motivations, intentions
• 3 Basic categories
• Informative (about the world)
• Motivational (what drives the agent)
• Deliberative (directs agent behaviour)

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Why Mental Attidues?

• First, if an agent can be described in terms of
  what it knows, what it wants, and what it
  intends, then,since it is modeled on familiar
  concepts, it becomes possible for users to
  understand and predict its behavior.
• Second, understanding the relationship between
  these different attitudes and how they affect
  behavior can provide the control mechanism for
  intelligent action in general.
• Third, computational agents designed in this way
  may be able to interpret the behavior of others
  independently from any implementation.

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BDI Theory and Architecture

• Supports many deliberative architectures
• Belief, desire and intention (theory)
• Represented as data structures (beliefs, goals
  and intentions) which determine the operation of
  an agent
• Cannot commit to all its goals as resource
  bounded so selects some which are the agents
  intentions

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Success of BDI

• Many formal models
• Generic agent architectures
• Basis for many previous and current agent
  architectures (including MAS)
• Successfully applied to more than just toy
  problems or simulations
• Air traffic control
• Customer-service applications (Agentis)
• First BDI system known as the Proceedural
  Reasoning System (PRS)

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The PRS Architecture
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PRS

• Beliefs, goals, intentions and plan library
• Agent perceive the world through external events
• Plans proceedural knowledge
• Recipes for action (tree labeled with actions and
  formulas which evaluate to a boolean)
• Trigger (what an agent must perceive)
• Context (what an agent must believe)
• If plans cannot proceed they post internal events

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From Plans to Intentions

• Agents respond to Internal and External events by
  selcting an appropriate plan in ts plan based
  whose trigger and context is true
• When a plan is adopted it becomes an intention
• This intention become part of the agents
  intention structure

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PRS Operation

• 1. Perceive the world, and update the set of
  events.
• 2. For each event, generate the set of plans
  whose trigger condition matches the event. These
  are known as the relevant plans of an event.
• 3. For each event, select the subset of relevant
  plans whose context condition is satisfied by the
  agents current beliefs. These plans are known as
  active plans.
• 4. From the set of active plans, select one for
  execution so that it is now an intention.
• 5. Include this new intention in the current
  intention structure either by creating a new
  intention stack or by placing it on the top of an
  existing stack.
• 6. Select an intention stack, take the topmost
  intention, and execute the next formula in it.

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AgentSpeak(L)

• Attempt to bridge the gap between BDI theories
  (highly abstract) and implemented BDI systems
• It is an abstraction of an implemented BDI system
  
• Captures essence of a PRS system

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AgentSpeak(L) Data Structures and Operation

• Beliefs
• Intentions (sequences of plans)
• Plans have a trigger, context and a sequence of
  actions and goals
• Agents instantiate plans in response to internal
  events (goals of executing plans) and external
  events (which effect a belief change)
• Agent execute the intentions

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Hybrid Agent Architectures

• Solely Reactive Systems
• Cannot reason about their actions
• Will never be able to achieve sophisticated
  behaviour
• Solely Deliberative Systems
• Will never be able to act in time
• If agents are going to be able to survive in
  complex dynamic environments and achieve complex
  goals they will need to be If agents are going to
  be able to survive in complex dynamic
  environments and achieve complex goals they will
  need to be both reactive and deliberative

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Touring Machines

• Based on the following observations
• Agents need the ability to deal with unexpected
  events in the real (physical or electronic)
  world, and do so at different levels of
  granularity.
• Agents need to deal with the dynamism in the
  environment created by the actions of other
  agents.
• Agents must pay attention to environmental
  change.
• Agents need to reason about temporal constraints
  in the knowledge that computation is necessarily
  resource-bounded.
• Agents must reason about the impact the
  short-term actions may have on long-term goals.

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TouringMachines Architectural Layers

• Reactive layer
• Responds to events not explicitly programmed in
  other layers such as obstacle avoidance
• Planning Layer
• Generating, executing and modifying plans
• Modelling Layer
• Builds and maintains models of other agents in
  the environment

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TouringMachine Architecture
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TouringMachines

• Each layer models world at different level of
  abstraction
• Each directly connected to perception and action
• Any two layers can communicate
• Global rules used when conflicts between layers
  arise and can surpress perception or action
• Important for showing how to build an agent which
  exhibited a range of behaviours from deliberative
  to reactive

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InterRRaP

• Another layered architecture
• Based on BDI
• It is vertically layered in constrast to
  TouringMachines which are horizontally layered
• Horizontally layered each layer can interact
  with every other layer and with perceptions and
  actions
• Vertically layered communication only possible
  between adjacent layers
• InterRRaP has four such layers
• The essential operation of the agent in response
  to events in the environment, control spreads
  upwards until the appropriate level is reached.

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InteRRaP Architecture
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Distributed Agent Architectures

• Agents described previously may have architecture
  that enables them to interact effectively with
  others at run time
• Here the design is concerned with individual
  agents and the dimensions required micro-level
  view
• Macro-level view considers a multiagent system
  from a holistic view, where interaction,
  coordination and cooperation between agents are
  designed in advance of run time

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Contract Net Protocol Overview

• Most commonly employed coordination mechanism for
  controlling the problem solving behavior of a
  collection of distributed agents
• concerned with the dynamic configuration and
  coordination of agents to form hierarchies that
  can achieve complex, distributed tasks.
• provides a mechanism by which nodes (or agents)
  can dynamically create relationships in response
  to the current processing requirements of the
  system as a whole, thereby enabling opportunistic
  task allocation.

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Contract Net Protocol

• 1. A node decomposes a task into a number of
  subtasks and issues a task announcement
  describing what needs to be performed with
  eligibility requirements
• 2. Free nodes bid if they fulfil the eligibility
  specification
• 3. Once all bids have been received they are
  ranked and a contract is awarded to the highest
  ranked bidder
• 4. There is now a contract between the manager
  who made the task announcement and the bidder
  (the contractor). Over time, the manager monitors
  the problem solving and integrates partial
  results
• 5. The manager terminates a contract with a
  termination message.

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Contract Net Protocol
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The Contract Net Hierarchy

• Subcontracting may take place

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Contract Net Summary

• The contract net protocol was a novel means for
  dynamically allocating tasks in distributed
  systems
• Still commonly found in many agent systems.
• However, it offers a very simple coordination
  mechanism
• does not provide any strategies for global
  coherence
• only suited to situations in which the tasks are
  easily decomposable into
• only suitable if agents are not in conflict
• does not support mechanisms for bargaining or
  negotiating
• We use it to illustrate general principles
  involved when considering the design of systems
  from the macrolevel perspective.

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Agentis Agent Communication Languages

• Agent Communication Languages
• FIPA ACL
• KQML
• Encodes
• Domain specific content
• Set of addressess
• Illocutionary
• Typically do not encode how a particular message
  should be used in the context of a wider
  communication episode
• Agentis includes set of agent protocols for
  reliable concurrent request and provision of
  services and tasks from and to agents.

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Agentis

• Agent Interaction Model
• Includes the notions of Services and Tasks
• Services are requested by human users
• Services may call upon a series of tasks as part
  of a service contract

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Other Approaches to MacroLevel Organisation

• DECAF
• Rapid design and execution of agents
• Focus on defining generic behaviours that can be
  re-used
• RETSINA
• a multiagent infrastructure that comprises a set
  of services, conventions, and knowledge that
  enable complex social interactions such as
  negotiation to take place
• TeamCore
• based on principle that an agent integration
  infrastructure (a set of services and
  conventions) based on theoretical principles of
  coordination can automate robust coordination
  among heterogeneous agents in distributed systems

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Other Agent Approaches Agent-Oriented
Programming

• Another approach to building agents is to design
  a programming language with semantics based on
  some theory of rational or intentional agency,
  such as the BDI model, and to program the desired
  behavior of individual agents directly using
  mental attitudes.
• Agent-Oriented Programming

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Agent-Oriented Programming

• program comprises a set of transition rules that
  specify how an agent in a given mental state will
  respond to an input, which may be a set of
  messages from other agents, by defining its new
  mental state and any outputs.
• One example is PLACA based on AGENT0 which takes
  a societal view (agents interact with each other
  to achieve their own goals)

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PLACA Program

• Agents state consists of capabilities and its
  mental state, which comprises beliefs,
  intentions, and plans.
• At every step, an agent collects messages that
  have been received from others and updates its
  mental state according to its defining program.
• At the beginning of each execution step, those
  transition rules that are satisfied in the
  current state are identified, and applied to the
  current mental state and messages collected from
  the input buffer.
• Once the mental state is updated, messages that
  need to be sent are placed in the output buffer,
  and actions that need to be performed are
  recorded and executed in the next step.
• If there is sufficient time before the next tick
  of the clock, the planner may construct and
  refine current plans for satisfying intentions.

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PLACA Interpreter
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Concurrent MeTaTeM

• Also takes a societal view
• Agents specified using a temporal logic that is
  directly executable
• Agents specified using temporal logic formula
• An interpreter attempts to construct a model in
  which the formula is true
• Dynamically changing environment also alters the
  model so responses must be made to what has just
  been made true or untrue in the model

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Specification of a Controller agent in Concurrent
MetaTeM

• 1. If an agent asks, then eventually give the
  resource to that agent
• 2. Do not give to anyone unless they have asked
  since you last gave to them
• 3. If you give to two people, then they must be
  the same person.

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Summary

• Deliberative vs Reactive
• design for decisions at run time - design occurs
  before run time
• Micro vs Macro
• design for decisions at run time - design occurs
  before run time