1. Lecture 1: Introduction Slides adapted from Sobah Abbas Petersen
CS 6100 MultiAgent Systems
Slides adapted from Sobah Abbas Petersen
(with permission)

2. Programming has progressed through
procedures & functions (no context)
abstract data types (data and operations combined) Reduces seen details
objects – data abstraction, encapsulation, modularity, polymorphism, and inheritance
to agents

3. The word 'agent' is widely used. Everyone knows what it means
The word 'agent' is widely used. Everyone knows what it means. We have press agents, travel agents, real estate agents, FBI agents, secret agents, double agents. Tom Cruise has an agent. Sometimes the word 'broker' refers to a kind of agent, for example, a stock broker.
But to actually define what the word, agent, means is not so easy. (Similar problems occur with other concepts such as intelligence, or life.) There seem to be almost as many definitions of agent, or agency as there are people trying to define these concepts!
So we will look at a few of these attempts at definition. Take your pick or make up your own.

4. Why Agents?
When a space probe makes its long flight from Earth to the outer planets, a ground crew is usually required to continually track its progress and decide how to deal with unexpected occurrences.
Expensive
if quick decisions needed, not practical
Nasa: make probes more autonomous
Not fiction! NASA’s DS1 is doing it now!

5. Ideas for agents
List an activity you engage it which could be taken over by a software agent…

6. Holiday Trip What aspects could it plan?
How would you communicate to system what you want it to do?

7. Overview Five ongoing trends have marked the history of computing:
Ubiquity – everywhere at once
Reduction in the cost of computing capability
Interconnection
Computer systems are networked into large distributed systems
Intelligence (MAS)
The complexity of tasks that can be automated and delegated to computers
Delegation (MAS)
Judgment of computer systems are frequently accepted
Human-orientation
Use concepts and metaphors that reflect how we understand the world. Examples?
Ubiquity and interconnection – global computing to deal with systems composed of a large number of processes.
Delegation and intelligence – systems that can act effectively on our behalf.
Delegation – act independently.
Intelligence – act in a way that represents our best interests while interacting with other humans or systems.

8. Concerns Need for computer systems to act in our best interests
“The issues addressed in Multiagent systems have profound implications for our understanding of ourselves.” Wooldridge

9. Trends…
These trends present major challenges to software developers. e.g.
Delegation – act independently.
Intelligence – act in a way that represents our best interests while interacting with other humans or systems.
Need systems that can act effectively on our behalf.
Systems must have the ability to cooperate and reach agreements with other systems.
New field: Multi-agent Systems

10. Two Key Problems
How do we build agents that are capable of independent, autonomous action in order to successfully carry out the tasks that we delegate to them? (Micro aspects)
How do we build agents that are capable of interacting (cooperating, coordinating, negotiating) with other agents in order to successfully carry out the tasks we delegate to them? (Macro aspects)
These are some of the things that we’ll be addressing in this course. By the end of the course, you’ll be able to design and implement agents that meet these requirements.

11. Problems How many of you play games to take advantage of the novice?
The other agent is as smart as you

12. Example – Market for Lemons
Suppose I have ten cars of varying quality. Only the seller knows its true value. The buyer cannot tell by looking.
Incentives exist for the seller to pass off a low-quality good as a higher-quality one.
But buyer is as smart as you (as the seller)!!!
The buyer, however, takes this incentive into consideration, and takes the quality of the good to be uncertain. Only the average quality of the good will be considered, which in turn will have the side effect that goods that are above average in terms of quality will be driven out of the market. This mechanism is repeated until a no-trade equilibrium is reached.
Note the different perspectives of the negotiation

13. Cars range from 1-10 in value (Only the seller knows value)
Buyer decides only to
offer average price (5.5)
Sellers of cars valued over 5.5 decide not to sell.

14. Cars range from 1-5 in value (Only the seller knows value)
Buyer decides only to
offer average price (3)
Sellers of cars valued over 3 decide not to sell.

15. Cars range from 1-3 in value (Only the seller knows value)
Buyer decides only to
offer average price (2)
Sellers of cars valued over 2 decide not to sell.

16. Cars range from 1-2 in value (Only the seller knows value)
Buyer decides only to
offer average price (1.5)
Sellers of cars valued over 1.5 decide not to sell.

17. Cars range from 1 in value (Only the seller knows value)
Buyer decides only to
offer average price (1)
Sellers decides to sell as that is a fair price.

18. Fields that inspired agents
Artificial Intelligence
Agent intelligence, micro aspects
Software Engineering
Agent as an abstraction
Distributed systems and Computer Networks
Agent architectures, multi-agent systems, coordination
There are many definitions of agents – often too narrow or too general.
Narrow – describing a particular agent in a particular situation
General – describing all types of software

19. Definitions of Agents... IBM:
”Intelligent agents are software entities that carry out some set of operations on behalf of a user or another program with some degree of independence or autonomy, and in doing so, employ some knowledge or representations of the user’s goals or desires”.

20. Definitions of Agents...
An agent is autonomous: capable of acting independently, exhibiting control over its internal state.
An agent is a computer system capable of autonomous action in some environment.
System
Input
Output
Environment

21. “An agent is a persistent software entity dedicated to a specific purpose. 'Persistent' distinguishes agents from subroutines; agents have their own ideas about how to accomplish tasks, their own agendas. 'Special purpose' distinguishes them from entire multifunction applications; agents are typically much smaller. (Smith, Cypher and Spohrer 1994)
It is widely recognized that interaction is probably the most important single characteristic of complex systems.
“Agents are programs that engage in dialogs, [and] negotiate and coordinate transfer of information.” (Michael Cohen)

22. At your seats
What is the difference between negotiate and coordinate?

23. “Agent enjoys the following properties:
autonomy - agents operate without the direct intervention of humans or others, and have some kind of control over their actions and internal state;
social ability - agents interact with other agents (and possibly humans) via some kind of agent-communication language;
reactivity: agents perceive their environment and respond in a timely fashion to changes that occur in it;
pro-activeness: agents do not simply act in response to their environment, they are able to exhibit goal-directed behaviour by taking initiative.” (Wooldridge and Jennings, 1995)

24. Properties of Agents
Mentalistic notions, such as beliefs and intentions are often referred to as properties of strong agents.
Other properties are:
Mobility: the ability of an agent to move around a network.
Veracity: agent will not knowingly communicate false information.
Benevolence: agents do not have conflicting goals and always try to do what is asked of it.
Rationality: an agent will act in order to achieve its goals and will not act in such a way as to prevent its goals being achieved.

25. Agents and Objects Are agents just objects by another name?
Remember the previous slide:
procedures & functions (no context)
abstract data types (data and operations combined)
objects
to agents
Are agents just objects by another name?

26. Agents and Objects Objects do it for free…
Agents do it because they want to!
Agents do it for money!

27. Agents and Objects... Main differences:
Agents are autonomous: agents embody a stronger notion of autonomy than objects, in particular, agents decide for themselves whether or not to perform an action.
Agents are smart: capable of flexible (reactive, pro-active social) behaviour; standard object models do not have such behaviour.
Agents are active: a multi-agent system is inherently multi-threaded in that each agent is assumed to have at least one thread of active control.

28. Why agents?
Today, we have a distributed environment that cannot be completely specified – open environments.
Former paradigms, such as OOP, cannot completely satisfy our needs:
They were designed for constructing systems in a completely specified environment - a closed world.

29. In the following examples, what is “open”?
lawn mowing
agent to search for miners
travel agent (cheaptickets.com)
ebay auction agent

30. Open Environment Lawn mowing – environment can change
Going to WalMart in an open environment
How can we work in an open environment?

31. How can we work in an Open Environment?
By copying human behaviour:
Perceive the environment
Affect the environment
Have a model of behaviour
Have intentions and motivations to be fulfilled by implementing corresponding goals
Agent
Environment

32. Decentralization An important concept
No central control; control is distributed
Knowledge or information sources may also be distributed. Why?

33. Multi-agent Systems (MAS)
Contains a number of agents which interact with one another through communication. The agents are able to act in an environment; where each agent will act upon or influence different parts of the environment. Reference: Wooldridge, An Introduction to Multiagent Systems
Environment
Multi-agent System

34. Motivation for MAS At seats: Explain or argue against?
To solve problems that are too large for a centralized agent
To allow interconnection and interoperation of multiple legacy systems
To provide a solution to inherently distributed problems
To provide solutions which draw from distributed information sources
To provide solutions where expertise is distributed (Why is expertise distributed?)
To offer conceptual clarity and simplicity of design
To be able to hide strategies from competition

35. Benefits of MAS Why? Faster problem solving Decrease in communication
Flexibility
Increased reliability - fault tolerant
Natural to Application
Decrease in communication: there might be an increase in the no. of messages passed or in the no. of interactions among the agents. But there is a decrease in communication as the volume of information transmitted each time will be reduced.

36. Summary
An agent is a system that is capable of independent action on behalf of its user or owner.
A multi-agent system is one that consists of a number of agents which interact with one another.
In order to successfully interact, agents need ability to cooperate, coordinate and negotiate.