Lecture 1 Introduction SIF 8072 Distributed Artificial Intelligence

Lecture 1: Introduction SIF 8072 Distributed Artificial Intelligence and Intelligent Agents http: //www. idi. ntnu. no/~agent/ Lecturer: Sobah Abbas Petersen Email: sap@idi. ntnu. no

Lecture Outline 1. Practical Information 2. Definition of an Agent 3. Distributed Artificial Intelligence and Multiagent Systems 4. Agent Typology 5. Summary and references 2

Practical Information - I Course-related information: Web-page – http: //www. idi. ntnu. no/~agent/ Lectures: Thursdays, 15: 00 -17: 00, room R 4 Web-page – http: //www. idi. ntnu. no/~agent/lectures/ Exercises: Mondays, 17: 00 -19: 00, room R 4 Web-page – http: //www. idi. ntnu. no/~agent/exercises/ Written Exam: Wednesday, 14 th May Web-page – http: //www. idi. ntnu. no/~agent/exam/ (past exam papers) 3

Practical Information - II Curriculum: • ”Introduction to Multi. Agent Systems” by Michael Wooldridge available from TAPIR, price: NOK 375 (http: //www. csc. liv. ac. uk/~mjw/pubs/imas/) • Additional Articles – List available from http: //www. idi. ntnu. no/~agent/curriculum/ Exercises and Project: 4 mandatory exercises and 1 mandatory project Questions regarding Exercises and Project: Teaching Assistant: Peep Kungas Email: peep@idi. ntnu. no 4

Lecture Plan Date Lecture 1 16. 01 2003 Introduction, Overview and Technology 2 23. 01 2003 Multi-agent Interactions 3 30. 01 2003 Negotiation 4 06. 02. 2003 Coordination 5 13. 02. 2003 Agent Communication Languages 6 20. 02. 2003 Agent Architectures 7 27. 02. 2003 Multi-agent Systems Architectures 8 06. 03. 2003 Agent Theory 9 13. 03. 2003 Mobile Agents 10 20. 03. 2003 Agent-oriented Software Engineering 11 27. 03. 2003 Agent-mediated Electronic Commerce 12 03. 04. 2003 Summary 5

Example 1 ”When a space probe makes its long flight from Earth to outer planets, a ground crew is usually required to continue to track its progress and decide how to deal with unexpected eventualities. This is costly and, if decisions are required quickly, it is simply not practical. For these reasons, organisations like NASA are seriously investigating the possibility of making the probes more autonomous – giving them richer decision making capabilities and responsibilities. ” 6

Example 2 ”Searching the Internet for the answer to a specific query can be a long and tedious process. So, why not allow a computer program – an agent – do searches for us? The agent would typically be given a query that would require synthesizing information from various different internet information sources. ” 7

Example 3 ”After a wet and cold winter, you are in need of a last minute holiday somewhere warm. After specifying your requirements to your Personal Digital Assistant (PDA), it converses with a number of different web sites which sell services such as flights and hotel rooms. After hard negotiation on your behalf with a range of sites, your PDA presents you with a package holiday. ” 8

Overview 1 • Five ongoing trends have marked the history of computing: 1. Ubiquity • Reduction in the cost of computing capability 2. Interconnection • Computer systems are networked into large distributed systems 3. Intelligence • The complexity of tasks that can be automated and delegated to computers 4. Delegation • Judgement of computer systems are frequently accepted 5. Human-orientation • Use concepts and metaphors that reflect how we understand the world 9

Overview 2 • 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. Ø Need systems that can act effectively on our behalf. • Systems must have the ability to cooperate and reach agreements with other systems. èNew field: Multi-agent Systems 10

Overview 3 • 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. 11

Two Key Problems 1. 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) 2. 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) 12

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. 13

Definitions of Agents 1 American Heritage Dictionary: ”. . . 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, Pattie: ”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”. 14

Definitions of Agents 2 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”. 15

Definitions of Agents 3 • An agent is autonomous: capable of acting independently, exhibiting control over its internal state. Ø An agent is a computer system capable of autonomous action in some environment. System Input Output Environment 16

Definition of Agent 4 • Examples of trivial/non-interesting agents are: – Thermostat, UNIX deamon, e. g. biff Ø An intelligent agent is a computer system capable of flexible autonomous action in some environment. - By flexible we mean: - Reactive - Pro-active - Social 17

Properties of Agents 1 • Autonomous – Capable of independent action without our interference • Reactive – Maintains an ongoing interaction with its environment and responds to changes that occur in (in time for the response to be useful). • Pro-active – Generating and attempting to achieve goals; not driven solely by events; taking the initiative. • Social – The ability to interact with other agents (and possibly humans) via some kind of agent communication language and perhaps cooperate with others. 18

Properties of Agents 2 • 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. 19

Agents and Objects 1 • Are agents just objects by another name? Objects do it for free… • Agents do it because they want to! • Agents do it for money! 20

Agents and Objects 2 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 multithreaded in that each agent is assumed to have atleast one thread of active control. 21

Let’s take a minute…… • Discuss with your neighbour what you think of this definition. • Try to come up with a few examples of agents that you know. 22

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. 23

How can we work in an Open Environment • By copying human behaviour: – Perceive the environment – Affect the environment – Have a model of behaviour Agent Environment – Have intentions and motivations to be fulfilled by implementing corresponding goals 24

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 areas of DAI 1. Multi-Agent Systems (MAS) 2. Distributed Problem Solving (DPS) Reference: B. Moulin, B. Chaib-draa. ”An Overview of Distributed Artificial Intelligence”. In: G. M. P. O'Hare, N. R. Jennings (eds). Foundations of Distributed Artificial Intelligence, John Wiley & Sons, 1996, pp. 3 -56. 25

DAI Concerns • DAI is concerned with: – Agent granularity – Heterogenity of agents – Methods of distributing control among agents – Communication possibilities • DAI is not concerned with: – Issues of coordination of concurrent processes at the problem solving and representational level – Parallel Computer Architectures, Parallel Programming Languages or Distributed Operating Systems 26

DPS and MAS • DPS considers how the task of solving a particular problem can be divided among a number of modules that cooperate in dividing and sharing knowledge about the problem and its evolving solution(s). • MAS is concerned with the behaviour of a collection of autonomous agents aiming to solve a given problem. 27

Decentralisation • An important concept in DAI and MAS – No central control; control is distributed – Knowledge or information sources may also be distributed 28

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, p. 105 Multi-agent System Environment 29

Motivation for MAS • 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 • To offer conceptual clarity and simplicity of design 30

Benefits of MAS • Faster problem solving • Decrease in communication • Flexibility • Increased reliability 31

Cooperative and Self-interested MAS • Cooperative – Agents designed by interdependent designers – Agents act for increased good of the system – Concerned with increasing the performance of the system • Self-interested – Agents designed by independent designers – Agents have their own agenda and motivation – Concerned with the benefit and performance of the individual agent Ø More realistic in an Internet setting? 32

Interaction and Communication in MAS • To successfully interact, agents need ability to cooperate, coordinate and negotiate. • This requires communication: – Plan /message passing – Information exchange using shared repositories • Important characteristics of communication: – Relevance of the information – Timeliness – Completeness 33

Let’s take a minute…… • Discuss with your neighbour: – A problem that can be solved by a MAS – Advantages and disadvantages of using a MAS for your particular problem 34

Agent Typology 1 • One of the most referred to typologies is given by Nwana, BT Research Labs – • Reference: H. S. Nwana. ”Software Agents: An Overview”, Knowledge Engineering Review, Vol. 11, No. 3, 1996, 40 pages Several dimensions of typology: – – – Mobility - mobile or static. Deliberative or reactive. Primary attributes, such as autonomy, learning and cooperation. 35

Agent Typology 2 • A part view of an agent typology Autonomous Software systems Learning Cooperate Collaborative Agents Smart Agents Interface Agents 36

Agent Typology 3 Nwana identified the following seven types of agents: 1. Collaborative agents - autonomous and cooperate. 2. Interface agents - autonomous and learn. 3. Mobile agents - able to move around a network. 4. Information/Internet agents - manages the information on the internet. 5. Reactive agents - stimulus-response behaviour. 6. Hybrid agents - combination two or more agent philosophies. 7. Smart agents - autonomous, learn and cooperate. • Criticisms of this Typology – Confuses agents with what they do (e. g. Info search) and the technology (e. g. reactive, mobile). 37

Agent Typology 4 • Collaborative agents – Hypothesis/Goal: The capabilities of the collection of agents is greater than any of its members. – Main Motivation: To solve problems that are too large for a single agent. • Interface agents – Hypothesis/Goal: A personal assistant that collaborates with the user. – Main Motivation: To eliminate humans performing several, manual sub -operatioions. – Example: A personal assistant that finds a suitable package holiday for the user. 38

Agent Typology 5 • Mobile agents – Hypothesis/Goal: Agents need not be stationary! – Main Motivation: To reduce communication costs – Example: Aglets • Information/Internet agents – Hypothesis/Goal: Reduce information overload problem – Main Motivation: The need for tools to manage information explosion – Example: agents that reside on servers and access the distributed on-line information on the Internet 39

Agent Typology 6 • Reactive agents – Hypothesis/Goal: Physical grounding hypothesis: representations grounded in the physical world. • Hybrid agents – Defn: constitutes a combination of two or more agent philosophies (e. g deliberative & reactive). – Hypothesis/Goal: Gains from the combination of philosophies >> gains from the same philosophy. 40

Agent Typology 7 Heterogeneous agents • Definition: System of agents of several types • e. g. mobile and interface agents in the same system • Realistic in an Internet (open-system) setting • Motivation: Interoperability is plausible • Requires Standards for communication among the agents: • Agent Communication Languages and protocols • Cooperation conventions 41

Other Types of Agents Some of these may not exhibit any agent properties as discussed earlier. • Desktop Agents: e. g. – Operating System agents – interact with the OS to perform tasks on behalf of the user. – • Application agents – e. g. Wizards Web search agents – act as information brokers between information suppliers (e. g. Websites) and information consumers (e. g. users) 42

Operating System Agents User Agent Application GUI Shell OS API Memory Mgmt. File Mgmt. Process Mgmt. 43

Web Search Agents User Query Web Browser Query Server Response Index database Web robot Search Engine 44

Information Filtering Agents User Web Browser News Server Indexed articles Web User Profile Indexing Engine Media Filtering Agent 45

Let’s take a minute…… • Discuss with your neighbour the main points in this lecture. 46

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. 47

Definition of Agents - Summary • • An agent acts on behalf of another user or entity An agent has the weak agent properties: – • An agent may have strong agent properties : – • autonomy, pro-activity, reactivity and social ability mentalistic notions such as beliefs and desires Other properties discussed in the context of agents: – mobility, veracity, benevolence and rationality 48

References • Curriculum: Wooldridge: ”Introduction to MAS” – • Chapters 1 & 2 Article: Agent Typology – H. S. Nwana. ”Software Agents: An Overview”, Knowledge Engineering Review, Vol. 11, No. 3, 1996, 40 pages • Recommended Reading (not curriculum) – B. Moulin, B. Chaib-draa. ”An Overview of Distributed Artificial Intelligence”. In: G. M. P. O'Hare, N. R. Jennings (eds). Foundations of Distributed Artificial Intelligence, John Wiley & Sons, 1996, pp. 3 -56. 49

Next Lecture: Multi-agent Interactions Will be based on: ”Multi-agent Interactions”, Chapter 6 in Wooldridge: ”Introduction to Multi. Agent Systems” 50

FBI Agents Ordering Pizza FBI agents conducted a raid of a psychiatric hospital in San Diego that was under investigation for medical insurance fraud. After hours of reviewing thousands of medical records, the dozens of agents had worked up quite an appetite. The agent in charge of the investigation called a nearby pizza parlour with delivery service to order a quick dinner for his colleagues. The following telephone conversation took place and was recorded by the FBI because they were taping all conversations at the hospital. Source: http: //jewel. morgan. edu/~salimian/humor_094. html 51

FBI Agents Ordering Pizza, contd. • Agent: Hello. I would like to order 19 large pizzas and 67 cans of soda. • Pizza Man: And where would you like them delivered? • Agent: We're over at the psychiatric hospital. • Pizza Man : The psychiatric hospital? • Agent : That's right. I'm an FBI agent. • Pizza Man : You're an FBI agent? • Agent : That's correct. Just about everybody here is. • Pizza Man : And you're at the psychiatric hospital? • Agent : That's correct. And make sure you don't go through the front doors. We have them locked. You will have to go around to the back to the service entrance to deliver the pizzas. • Pizza Man : And you say you're all FBI agents? • Agent : That's right. How soon can you have them here? • Pizza Man : And everyone at the psychiatric hospital is an FBI agent? • Agent : That's right. We've been here all day and we're starving. • Pizza Man : How are you going to pay for all of this? • Agent : I have my checkbook right here. • Pizza Man : And you're all FBI agents? • Agent : That's right. Everyone here is an FBI agent. Can you remember to bring the pizzas and sodas to the service entrance in the rear? We have the front doors locked. • Pizza Man : I don't think so. Click. 52