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Chapter 19Intelligent Agents - PowerPoint PPT Presentation

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Chapter 19 Intelligent Agents. AI Agents . Chapter 19 Contents (1). Intelligence Autonomy Ability to Learn Other Agent Properties Reactive Agents Utility-Based Agents Utility Functions Interface Agents Mobile Agents.

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Chapter 19

Intelligent Agents

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AI Agents


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Chapter 19 Contents (1)

  • Intelligence

  • Autonomy

  • Ability to Learn

  • Other Agent Properties

  • Reactive Agents

  • Utility-Based Agents

  • Utility Functions

  • Interface Agents

  • Mobile Agents

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Chapter 19 Contents (2)

  • Information Agents

  • Multiagent Systems

  • Subsumption Architecture

  • BDI Architectures

  • Horizontal and Vertical Architectures

  • Accessibility

  • Learning Agents

  • Robotic Agents

  • Braitenberg Vehicles

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  • An agent is a tool that carries out tasks on behalf of a human user.

  • An intelligent agent possesses domain knowledge and the ability to use that knowledge to solve its problems more efficiently.

  • Intelligent agents are often able to learn, and have other properties that we will look at in the following slides.

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  • Autonomy is the ability to act independently of the human user’s instructions.

  • Hence, a buying agent that needs to make a quick decision about an increased bid can use autonomy to do so without the need to waste time by consulting a human.

  • Autonomy is a an important feature of many intelligent agents, but is not seen in many other Artificial Intelligence techniques.

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Ability to Learn

  • Many agents can learn from their environments and from their success or failure at solving problems.

  • Agents can learn from a user or from other agents.

  • When a human tells an agent it has solved a problem poorly it can learn from this and avoid making the same mistakes in the future.

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Other Agent Properties

  • Co-Operation: interaction between agents.

  • Versatility: ability to carry out a range of different tasks.

  • Benevolence: helpfulness to other agents and people.

  • Veracity: tendency to tell the truth.

  • Mobility: ability to move about in the Internet or another network (or the real world).

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Reactive Agents

  • Also known as reflex agents.

  • Uses a production system to determine what action to carry out based on current inputs.

  • Example: spam mail filter.

  • Does not perform well when the environment changes.

  • Does not deal well with unexpected events.

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Utility-Based Agents

  • Agents that attempt to achieve some specified goal, usually using search or planning methods.

  • An agent, for example, might have the goal of finding interesting web pages.

  • The agent would have various actions it could perform such as fetching web pages and examining them.

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Utility Functions (1)

  • More sophisticated goal-based agents have utility functions to decide which goals to accept.

  • The agent is always attempting to both achieve its goals, and to maximize some utility function.

  • Hence, the web researching agent would have a utility function that measured how interesting web pages were, and would attempt to find the most interesting page it could.

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Utility Functions (2)

  • A utility function maps the set of states to the set of real numbers.

  • Hence, an agent with a utility function can determine how “happy” it is in any given state.

  • Example: Static board evaluators used in playing games.

  • A rational agent is one that will always try to maximize its utility functions.

    • This is true even if this results in seemingly bizarre behavior.

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Interface Agents

  • An interface agent is a personal assistant.

  • Example: a tool used to help a user learn to use a new software package.

  • Interface agents observe a user’s behavior and make recommendations accordingly.

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Mobile Agents

  • Mobile agents can move from one location to another.

  • This can mean physical locations (for robots) or network locations.

  • A computer virus is a kind of mobile agent. Viruses are usually autonomous but not intelligent.

  • Mobile agents are efficient, but can pose a severe security risk.

  • Mobile agents can be combined to produce a distributed computing architecture.

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Information Agents

  • Also known as Internet agents.

  • Information agents gather information from the Internet (or other source of data).

  • Can be static or mobile.

  • Can be taught by example: “find me more information like this”.

  • Information agents need to be sophisticated to deal with the “dirty” nature of much of the data on the Internet.

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Multi-agent Systems (1)

  • A multi-agent system depends on a number of agents.

  • Each agent has incomplete information and cannot solve the problem on its own.

  • By cooperating, all the agents together can solve the problem.

  • Similar to the way in which ant colonies work.

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Multi-agent Systems (2)

  • Agents in multi-agent systems usually have the ability to communicate and collaborate with each other.

  • Learning multi-agent systems can be developed, for example to control the individual limbs of a robot.

  • An agent team is a group of agents that co-operate to achieve some common goal – such as arranging the various components of a trip: flight, train, taxi, hotel etc.

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Subsumption Architecture (1)

  • Architecture for intelligent agents – invented by Brooks in 1985.

  • Consists of a set of inputs, outputs and modules in layers. For example:

  • Each module is an AFSM (Augmented Finite State Machine) – based on production rules of the form input -> action.

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Subsumption Architecture (2)

  • The rules are situated action rules, as they determine what the agent will do in given situations.

  • Such an agent is said to be situated.

  • An AFSM triggers when its input exceeds a threshold.

  • The layers in the architecture act asynchronously, but can affect each other.

  • One layer can suppress the outputs of some layers, while taking into account output from other layers.

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BDI Architectures

  • Belief Desire Intention Architectures.

    • Beliefs: statements about the environment.

    • Desires: goals

    • Intentions: plans for how to achieve the goals.

  • The agent considers the options available, and commits to one.

  • This option becomes the agent’s intention.

  • Agents can be bold (carries out its intentions no matter what) or cautious (constantly reassesses its intentions).

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Horizontal and Vertical Architectures

  • The subsumption architecture and TouringMachines are examples of horizontal architectures:

  • Layers act in parallel and all contribute to an overall output.

  • InteRRaP is an example of a vertical layered architecture:

    • Outputs are passed through from one layer to the next, until the last layer produces the final output.

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    • Some agents operate in accessible environments, where all relevant facts are available to the agent

    • Most agents must operate in inaccessible environments where some information is unavailable.

    • For example, chess playing is accessible, poker playing is inaccessible.

    • Additionally, environments can be deterministic or stochastic.

    • Markov Decision Processes are useful for dealing with stochastic, accessible environments.

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    Learning Agents

    • Agents learn using mechanisms such as neural networks and genetic algorithms.

    • Learning enables an agent to solve problems it has not previously faced, and to learn from past experience.

    • Multi-agent learning can produce much more impressive results.

    • Such learning can be centralized or decentralized – agents learn individually or contribute to the learning of the whole group.

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    Robotic Agents

    • Unlike software agents, robotic agents exist in the real world.

    • Robots operate in a stochastic, inaccessible environment, and must also be able to deal with large numbers of other agents (such as humans) and other complicating factors.

    • It is important for robotic agents to deal with change and uncertainty well.

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    Braitenberg Vehicles

    • Simple robotic agents that can exhibit complex behavior.

    • There are 14 classes of vehicles.

    • Class 1: simply moves faster the more light there is.

    • Class 2: two configurations – one moves towards light, the other away.

    • These can be thought of as being bold and timid.