1. Course overview and what is intelligent agent
Agent Technology
Course overview
and
what is intelligent agent

2. Before we start Software Agent: Prof. Tao Xianping
Intelligent Agent: A. Prof., Dr. Gao Yang
(O)
Ai Lab, CS Dept., NJU
Room 403-A, Mengminwei Building
Courseware could be found from my homepage.
©Gao Yang, Ai Lab NJU
Sept. 2008

3. Motivation Agents, the next paradigm for software?
Agent-Oriented taking over for Object-Oriented?
Agents is crucial for open distributed systems?
Agents the most natural entity in e-business and other e-***?
Agent and peer-to-peer, sensor network technologies inseparable?
Which is the killer application using the agent technology?
©Gao Yang, Ai Lab NJU
Sept. 2008

4. What will you learn from this course?
Upon completed this course a student should
Know what an agent and an agent system is.
Have a good overview of important agent issues:
Agent Negotiation, Coordination and Communication.
Micro and macro agent Architectures.
Agent Learning.
Agent Model and Theory.
Agent-oriented Software Engineering.
Get valuable hands-on experience in developing agent system.
©Gao Yang, Ai Lab NJU
Sept. 2008

5. Lectures: Part A
1st Week Course overview and what is intelligent agent
2nd Week Negotiation in MAS(i)
3rd Week Negotiation in MAS(ii)
4th Week Agent learning (i)
5th Week Agent learning (ii)
6th Week Agent communication language
7th Week Application: RoboCup, Trading Agent Competition & Intelligent Game
8th Week Agent architectures
9th Week Agent model and theory
©Gao Yang, Ai Lab NJU
Sept. 2008

6. Other Issues Other issues: Architectures of multi-agent system(Macro)
Coordination in MAS
Agent oriented software engineering
Agent oriented programming
Agent and p2p computing
Agent and Grid computing
Classification of agents and its application
©Gao Yang, Ai Lab NJU
Sept. 2008

7. Recommended books
Michael Wooldridge. “An Introduction to MultiAgent Systems”, 2002
Shi Zhong-zhi. “Intelligent Agent and Its Application” (in Chinese). Science press, 2000.
G.Weiss, editor. "Multiagent Systems". MIT Press, 1999.
J. Ferber. "Multi-Agent Systems". Addison-Wesley, 1999.
G. M. P. O'Hare and N. R. Jennings, editors. "Foundations of Distributed AI". Wiley Interscience, 1996.
M. Singh and M. Huhns. "Readings in Agents". Morgan-Kaufmann Publishers, 1997.
And other choiced papers and websites.
©Gao Yang, Ai Lab NJU
Sept. 2008

8. Assessment Lecturee 10% Experiments 30% Final Exam(open) 60%
©Gao Yang, Ai Lab NJU
Sept. 2008

9. What is intelligent agent
Field that inspired the agent fields?
Artificial Intelligence
Agent intelligence and micro-agent
Software Engineering
Agent as an abstract entity
Distributed System and Computer Network
Agent architecture, MAS, Coordination
Game Theory and Economics
Negotiation
There are two kinds definition of agent
Often quite narrow
Extremely general
©Gao Yang, Ai Lab NJU
Sept. 2008

10. General definitions American Heritage Dictionary Russel and Norvig
”... One that acts or has the power or authority to act ... or represent another”
Russel and Norvig
”An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors.”
Maes, Parrie
”Autonomous agents are computational systems that inhabit some complex dynamic environment, sense and act autonomously in this environment, and by doing so realize a set of goals or tasks for which they are designed”.
©Gao Yang, Ai Lab NJU
Sept. 2008

11. Agent: more specific definitions
Smith, Cypher and Spohrer
”Let us define an agent as 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 multifunction applications; agents are typically much smaller.
Hayes-Roth
”Intelligent Agents continuously perform three functions: perception of dynamic conditions in the environment; action to affect conditions in the environment; and reasoning to interpret perceptions, solve problems, draw inferences, and determine actions.
©Gao Yang, Ai Lab NJU
Sept. 2008

12. Agent: industrial definitions
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”
©Gao Yang, Ai Lab NJU
Sept. 2008

13. Agent: weak notions Wooldridge and Jennings
An Agent is a piece of hardware or (more commonly) software-based computer system that 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;
Pro-activeness: agents do not simply act in response to their environment, they are able to exhibit goal-directed behavior by taking the initiative.
Reactivity: agents perceive their environment and respond to it in timely fashion to changes that occur in it.
Social Ability: agents interact with other agents (and possibly humans) via some kind of agent-communication language.”
©Gao Yang, Ai Lab NJU
Sept. 2008

14. Agent: strong notions Wooldridge and Jennings
Weak notion in addition to
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
Veracity: 诚实
Benevolence: 仁慈的
©Gao Yang, Ai Lab NJU
Sept. 2008

15. Summary of agent definitions
An agent act on behalf user or another entity.
An agent has the weak agent characteristics. (Autonomy, Pro-activeness, Reactivity, Social ability)
An agent may have the strong agent characteristics. (Mobility, Veracity, Benevolence, Rationality)
©Gao Yang, Ai Lab NJU
Sept. 2008

16. Dear child gets many names…
Many synonyms of the term “Intelligent agent”
Robots
Software agent or softbots
Knowbots
Taskbots
Userbots ……
©Gao Yang, Ai Lab NJU
Sept. 2008

17. Why the buzz around the agents?
Lack of programming paradigm for distributed systems.
Tries to meet problems of the “closed world” assumption in object-orientation.
Agents is a frequently used term to describe software in general (due to vague definition) .
Massive media hype in the era of the dot-coms.
Close-world as in full control
©Gao Yang, Ai Lab NJU
Sept. 2008

18. Autonomy is the key feature of agent
Examples
Thermostat
Control / Regulator
Any control system
Software Daemon
Print server
Http server
Most software daemons
Thermostat: 自动调温器
©Gao Yang, Ai Lab NJU
Sept. 2008

19. Thinking…
Give other examples of agents (not necessarily intelligent) that you know of. For each, define as precisely as possible:
(a). the environment that the agent occupies, the states that this environment can be in, and the type of environment.
(b). The action repertoire available to the agent, and any pre-conditions associated with these actions;
(c). The goal, or design objectives of the agent – what it is intended to achieve.
©Gao Yang, Ai Lab NJU
Sept. 2008

20. Thinking again…
If a traffic light (together with its control system) is considered as intelligent agent, which of agent’s properties should be employ? Illustrate your answer by examples.
©Gao Yang, Ai Lab NJU
Sept. 2008

21. Type of environment
An agent will not have complete control over its environment, but have partial control, in that it can influence it.
Scientific computing or MIS in traditonal computing.
Classification of environment properties [Russell 1995, p49]
Accessible vs. inaccessible
Deterministic vs. non-deterministic
Episodic vs. non-episodic
Static vs. dynamic
Discrete vs. continuous
©Gao Yang, Ai Lab NJU
Sept. 2008

22. Accessible vs. inaccessible
An accessible environment is one in which the agent can obtain complete, accurate, up-to-date information about the environment’s state. (also complete observable vs. partial observable)
Accessible: sensor give complete state of the environment.
In an accessible environment, agent needn’t keep track of the world through its internal state.
©Gao Yang, Ai Lab NJU
Sept. 2008

23. Deterministic vs. non-deterministic
A deterministic environment is one in which any action has a single guaranteed effect , there is no uncertainty about the state that will result from performing an action.
That is, next state of the environment is completely determined by the current state and the action select by the agent.
Non-deterministic: a probabilistic model could be available.
©Gao Yang, Ai Lab NJU
Sept. 2008

24. Episodic vs. non-episodic
In an episodic environment, the performance of an agent is dependent on a number of discrete episodes, with no link between the performance of an agent in different scenarios. It need not reason about the interaction between this and future episodes. (such as a game of chess)
In an episodic environment, agent doesn’t need to remember the past, and doesn’t have to think the next episodic ahead.
Everything it needs to know is the current state’s percept.
©Gao Yang, Ai Lab NJU
Sept. 2008

25. Static vs. dynamic Static vs. dynamic
A static environment is one that can assumed to remain unchanged expect by the performance of actions by the agents.
A dynamic environment is one that has other processes operating on it which hence changes in ways beyond the agent’s control.
©Gao Yang, Ai Lab NJU
Sept. 2008

26. Discrete vs. continuous
An environment is discrete if there are a fixed, finite number of actions and percepts in it.
©Gao Yang, Ai Lab NJU
Sept. 2008

27. Why classify environments
The type of environment largely determines the design of agent.
Classifying environment can help guide the agent’s design process (like system analysis in software engineering).
Most complex general class of environments
Are inaccessible, non-deterministic, non-episodic, dynamic, and continuous.
©Gao Yang, Ai Lab NJU
Sept. 2008

28. Discuss about environment: Gripper
Gripper is a standard example for probabilistic planning model
Robot has three possible actions: paint (P), dry (W) and pickup (U)
State has four binary features: block painted, gripper dry, holding block, gripper clean
Initial state:
Goal state:
©Gao Yang, Ai Lab NJU
Sept. 2008

29. Discuss about environment: Gripper
©Gao Yang, Ai Lab NJU
Sept. 2008

30. Thinking… Please determine the environment’s type. Chess Poker
Mine-sweeper
E-shopping
Accessible??
Deterministic??
Episodic??
Static??
Discrete??
©Gao Yang, Ai Lab NJU
Sept. 2008

31. Intelligent agent vs. agent
An intelligent agent is one that is capable of flexible autonomous action in order to meet its design objectives, where flexibility means three things:
Pro-activeness: the ability of exhibit goal-directed behavior by taking the initiative.
Reactivity: the ability of percept the environment, and respond in a timely fashion to changes that occur in it.
Social ability: the ability of interaction with other agents (include human).
©Gao Yang, Ai Lab NJU
Sept. 2008

32. Pro-activeness Pro-activeness
In functional system (goal must remain valid at least until the action complete.), apply pre-condition and post-condition to realize goal directed behavior.
But for non-functional system (dynamic system), agent blindly executing a procedure without regard to whether the assumptions underpinning the procedure are valid is a poor strategy.
Observe incompletely
Environment is non-deterministic
Other agent can affect the environment
©Gao Yang, Ai Lab NJU
Sept. 2008

33. Reactivity Reactivity
Agent must be responsive to events that occur in its environment.
Building a system that achieves an effective balance between goal-directed and reactive behavior is hard.
©Gao Yang, Ai Lab NJU
Sept. 2008

34. Social ability Social ability
Must negotiate and cooperate with others.
©Gao Yang, Ai Lab NJU
Sept. 2008

35. Agent vs. object
Object
Are defined as computational entities that encapsulate some state, are able to perform actions, or methods on this state, and communicate by message passing.
Are computational entities.
Encapsulate some internal state.
Are able to perform actions, or methods, to change this state.
Communicate by message passing.
©Gao Yang, Ai Lab NJU
Sept. 2008

36. Agent and object Differences between agent and object
An object can be thought of as exhibiting autonomy over its state: it has control over it. But an object does not exhibit control over it’s behavior.
Other objects invoke their public method. Agent can only request other agents to perform actions.
“Objects do it for free, agents do it for money.”
(implement agents using object-oriented technology)……Thinking it.
©Gao Yang, Ai Lab NJU
Sept. 2008

37. Agent and object
In standard object model has nothing whatsoever to say about how to build systems that integrate reactive, pro-active, social behavior.
Each has their own thread of control. In the standard object model, there is a single thread of control in the system.
(agent is similar with an active object.)
Summary,
Agent embody stronger notion of autonomy than object
Agent are capable of flexible behavior
Multi-agent system is inherently multi-threaded
©Gao Yang, Ai Lab NJU
Sept. 2008

38. Agent and expert system
Is one that is capable of solving problems or giving advice in some knowledge-rich domain.
The most important distinction
Expert system is disembodied, rather than being situated.
It do not interact with any environment. Give feedback or advice to a third part.
Are not required to interact with other agents.
©Gao Yang, Ai Lab NJU
Sept. 2008

39. Example of agents ©Gao Yang, Ai Lab NJU Sept. 2008
Each peer is a software agent representing a mobile customer on the Internet, agent gets queries (implicit by customer browsing/purchases or explicitly by customer-agent interaction, then agent passes query on to its neighbour peers and receives recommendations from other peers.
©Gao Yang, Ai Lab NJU
Sept. 2008

40. Distributed Artificial Intelligence (DAI)
DAI is a sub-field of AI
DAI is concerned with problem solving where agents solve (sub-) tasks (macro level)
Main area of DAI
Distributed problem solving (DPS)
Centralized Control and Distributed Data (Massively Parallel Processing)
Multi-agent system (MAS)
Distributed Control and Distributed Data (coordination crucial)
Some histories
©Gao Yang, Ai Lab NJU
Sept. 2008

41. DAI is concerned with……
Agent granularity (agent size)
Heterogeneity agent (agent type)
Methods of distributing control (among agents)
Communication possibilities
MAS
Coarse agent granularity
And high-level communication
©Gao Yang, Ai Lab NJU
Sept. 2008

42. DAI is not concerned with……
Issues of coordination of concurrent processes at the problem solving and representational level.
Parallel computer architecture, parallel programming languages or distributed operation system.
No semaphores, monitors or threads etc.
Higher semantics of communication (speech-act level)
©Gao Yang, Ai Lab NJU
Sept. 2008

43. Motivation behind MAS
To solve problems too large for a centralized agent
E.g. Financial system
To allow interconnection and interoperation of multiple legacy system
E.g. Web crawling
To provide a solution to inherently distributed system
To provide a solution where expertise is distributed
To provide conceptual clarity and simplicity of design
©Gao Yang, Ai Lab NJU
Sept. 2008

44. Benefits of MAS Faster problem solving Decreasing communication
Higher semantics of communication (speech-act level)
Flexibility
Increasing reliability
©Gao Yang, Ai Lab NJU
Sept. 2008

45. Heterogeneity degrees in MAS
Low
Identical agents, different resources
Medium
Different agent expertise
High
Share only interaction protocol (e.g. FIPA or KQML)
©Gao Yang, Ai Lab NJU
Sept. 2008

46. Cooperative and self-interested MAS
Agents are designed by interdependent designers
Agents act for increased good of the system (i.e. MAS)
Concerned with increasing the systems performance and not the individual agents
Self-interested
Agents are designed by independent designer
Agents have their own agenda and motivation
Concerned with the benefit of each agent (’individualistic’)
The latter more realistic in an Internet-setting?
©Gao Yang, Ai Lab NJU
Sept. 2008

47. Our categories about MAS
Cooperation
Both has a common object
Competitive
Each have different objects which are contradictory.
Semi-competitive
Each have different objects which are conflictive, but the total system has one explicit (or implicit) object
The first now is known as TEAMWORK.
©Gao Yang, Ai Lab NJU
Sept. 2008

48. Distributed AI perspectives
©Gao Yang, Ai Lab NJU
Sept. 2008

49. Our Thinking in MAS Single benefit vs. collective benefit
No need central control
Social intelligence vs. single intelligence
Self-organize system
Self-form, self-evolve
Intelligence is emergence, not innative
…..
How do structure form? How do orderliness form?
Intelligence is emergence, not innative.
©Gao Yang, Ai Lab NJU
Sept. 2008

50. Conclusions of lecture
Agent has general definition, weak definition and strong definition
Classification of the environment
Differences between agent and intelligent agent, agent and object, agent and expert system
Multi-agent system is macro issues of agent systems
©Gao Yang, Ai Lab NJU
Sept. 2008

51. References
[Russell 1995] S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice-Hall, 1995.
©Gao Yang, Ai Lab NJU
Sept. 2008