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Chapter 19 Intelligent Agents. AI Agents . http://www.aaai.org/AITopics/html/agents.html. 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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slide1
Chapter 19

Intelligent Agents

ai agents
AI Agents
  • http://www.aaai.org/AITopics/html/agents.html
chapter 19 contents 1
Chapter 19 Contents (1)
  • Intelligence
  • Autonomy
  • Ability to Learn
  • Other Agent Properties
  • Reactive Agents
  • Utility-Based Agents
  • Utility Functions
  • Interface Agents
  • Mobile Agents
chapter 19 contents 2
Chapter 19 Contents (2)
  • Information Agents
  • Multiagent Systems
  • Subsumption Architecture
  • BDI Architectures
  • Horizontal and Vertical Architectures
  • Accessibility
  • Learning Agents
  • Robotic Agents
  • Braitenberg Vehicles
intelligence
Intelligence
  • 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.
autonomy
Autonomy
  • 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.
ability to learn
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.
other agent properties
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).
reactive agents
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.
utility based agents
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.
utility functions 1
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.
utility functions 2
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.
interface agents
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.
mobile agents
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.
information agents
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.
multi agent systems 1
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.
multi agent systems 2
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.
subsumption architecture 1
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.
subsumption architecture 2
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.
bdi architectures
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).
horizontal and vertical architectures
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.
accessibility
Accessibility
  • 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.
learning agents
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.
robotic agents
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.
braitenberg vehicles
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.
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