Chapter 19 Intelligent Agents

1. Chapter 19 Intelligent Agents

2. AI Agents • http://www.aaai.org/AITopics/html/agents.html

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

4. Chapter 19 Contents (2) • Information Agents • Multiagent Systems • Subsumption Architecture • BDI Architectures • Horizontal and Vertical Architectures • Accessibility • Learning Agents • Robotic Agents • Braitenberg Vehicles

5. 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.

6. 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.

7. 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.

8. 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).

9. 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.

10. 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.

11. 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.

12. 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.

13. 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.

14. 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.

15. 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.

16. 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.

17. 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.

18. 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.

19. 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.

20. 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).

21. 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.

22. 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.

23. 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.

24. 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.

25. 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.