Agent-Based%20Acceptability-Oriented%20Computing

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Agent-Based Acceptability-Oriented Computing

• International Symposium on Software Reliability
  Engineering
• Fast Abstract by Shana Hyvat

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Reliability vs. Functionality

• As software becomes more complex insuring its
  reliability becomes more challenging.
• Increasing functionality increases potentials
  for errors and complications that may arise from
  those errors.

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Proposed Solutions

• Rinards 2003 Acceptability-Oriented Computing
• Goal To achieve flexibility in programming
  while ensuring the system runs reliability

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Correct vs. Acceptable Behavior

• As systems become more complex, unrealistic to
  presume correct functionality.
• Maintaining a system with acceptable
  functionality is more realistic.

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Acceptable Behavior

• Program designer must specify functionality
• States of acceptable behavior must be identified
• example
• Particular error doesnt lead to a crash but to
  a stop.

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Rinards Architecture

• Core specifies the functionality
• Intended to completely specify both the behavior
  of the system and the structure required to
  completely implement the behavior.
• Remains unreliable by itself.

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Rinards Architecture

• Outer Layers
• Enforce acceptable system behavior and structure
  properties.
• Identify impending violation of the desired
  acceptability properties.
• Restores and maintains program behavior within
  the acceptability envelope.

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Enforcement

• Resilient Approach
• Takes actions to restore system to an acceptable
  state.
• Example
• Memory is full, release old data.
• Safe Exit Approach
• Allows a program to stop before executing
  improperly
• These approaches will be independent to each
  system and will depend of what the system
  designer decides in acceptable.

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Components

• Components are modules within outer layers will
  monitor, correct, and record errors in
  executions.
• We introduce
• Intelligent components in the form of
  Monitor-Agents that will perform similarly to
  Rinards

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Properties of Agents

• Autonomous/Independent
• Reactive to their environment
• Pro-active and work towards a goal
• Social Ability that allows them to communicate
  with other agents (may be human of software
  entities).

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• It will be easy to see how Rinards concept
  matches well with the properties of agents.

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Autonomy

• Re-engineering an entire system for greater
  reliability may not be practical in many
  instances, but amending a system with a separate,
  autonomous component, such as an agent, can prove
  to be a more viable solution.

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Reactive

• When an error occurs the agent will be able to
  detect and choose a solution for repair or for a
  safe exit.
• In addition, its behavior will be
  intelligent.
• Pattern recognition will be a characteristic of
  this intelligence.

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Pro-active

• The pro-active, goal driven behaviour of an
  agent will be to acquire intelligence through
  learning in the form of pattern recognition.
• Neural Networks has been shown viable in the
  field of pattern recognition.

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Pattern Recognition

• Rinards method, errors are logged for the
  systems designers use
• We give the monitor-agent ability to log errors
  as well as recognize patterns in them.
• Recognize a sequence of errors that always emerge
  when taking a particular resilient approach and
  to avoid this sequence may choose a different
  resilient approach to an error.

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Interaction

• interaction is the single most important
  characteristic in complex systems
• - Wooldridge and Ciancarini

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Errors in Interactions

• A request to access non-existent memory or
  simple spelling errors are such examples.
• Utilize the social ability of the agent to
  translate or repair messages to the core from
  the environment of the system.
• The translation is from an input that can
  possibly lead to an error-prone execution to one
  that will lead to an acceptable execution.

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Designing the System

• Gaia Method Wooldridge and Ciancarini
• Focuses on the problem solving nature of agents
  and organizes agents to communicate with each
  other and with their world .
• Method of design of an agent-based system.

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Gaia Analysis Process

• Define roles in the system
• detect error when it occurs
• log the error
• learn from the error
• choose a resilient solution or a safe exit
  solution

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Defining Gaia Models

• In addition to goals we define models for the
  system.
• Since we are using a single agent-based systems,
  there is one model
• services model, which is the interface to the
  core.

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Services Model

• Services model as a separate component to the
  agent-monitor.
• The services model will be independent to a
  particular core.
• In this way we can create a monitor-agent that
  can be augmented to any existing system.
• We refer to any existing system as a unreliable
  core, given the assumption that we are augmenting
  a system due some reliability issues.