1 Computer Science Department California Polytechnic State University San Luis Obispo, CA, U.S.A. Franz J. Kurfess CPE/CSC 580: Intelligent Agents 1

2 © Franz J. Kurfess Usage of the Slides  these slides are intended for the students of my CPE/CSC 580 “Intelligent Agents” class at Cal Poly SLO ◆ if you want to use them outside of my class, please let me know ◆ some of them are based on other sources, which are identified and cited  I usually select a subset for each quarter, either by hiding some slides, or creating a “Custom Show” (in PowerPoint) ◆ to view these, go to “Slide Show => Custom Shows”, select the respective quarter, and click on “Show”  To print them, I suggest to use the “Handout” option ◆ 4, 6, or 9 per page works fine ◆ Black & White should be fine; there are few diagrams where color is important

3 © Franz J. Kurfess Course Overview ❖ Introduction  Intelligent Agent, Multi-Agent Systems  Agent Examples ❖ Agent Architectures  Agent Hierarchy, Agent Design Principles ❖ Reasoning Agents  Knowledge, Reasoning, Planning ❖ Learning Agents  Observation, Analysis, Performance Improvement ❖ Multi-Agent Interactions  Agent Encounters, Resource Sharing, Agreements ❖ Communication  Speech Acts, Agent Communication Languages ❖ Collaboration  Distributed Problem Solving, Task and Result Sharing ❖ Agent Applications  Information Gathering, Workflow, Human Interaction, E-Commerce, Embodied Agents, Virtual Environments ❖ Conclusions and Outlook

4 © Franz J. Kurfess Overview Introduction ❖ Motivation ❖ Objectives ❖ What is an Intelligent Agent?  Intelligence, Autonomy ❖ Towards Agents  Trends in Computing ❖ Definitions and Concepts  Agent, Multi-Agent System, Environments ❖ Agent Examples  Robots and Embodied Agents, E-Commerce, Personal Assistants ❖ Research Issues ❖ Important Concepts and Terms ❖ Chapter Summary

5 © Franz J. Kurfess Logistics  Introductions  Course Materials ◆ textbooks (see below) ◆ lecture notes ◆ PowerPoint Slides will be available on my Web page ◆ handouts ◆ Web page ◆  Term Project  Lab and Homework Assignments  Exams  Grading

6 © Franz J. Kurfess Textbooks  Main Textbook  Recommended for additional reading ❖ [Woolridge 2009] Michael Woolridge, An Introduction to Multi-Agent- Systems, Wiley, ❖ [Russell & Norvig 2009] Stuart Russell and Peter Norvig, Artificial Intelligence - A Modern Approach. 3 nd ed., Prentice Hall, 2009.

7 © Franz J. Kurfess Bridge-In

8 © Franz J. Kurfess Pre-Test

9 © Franz J. Kurfess Motivation ❖ introduce the concept of an agent and multi-agent system ❖ examine intelligent autonomous agents  main approaches and techniques for the implementation of such agents ❖ investigate multi-agent systems  main approaches and techniques communication and cooperation in such systems; ❖ explore aspects of a multi-agent society  automated decision making in multi-agent contexts

10 © Franz J. Kurfess Objectives ❖ Understand the fundamental concepts in the study of intelligent agents. ❖ Be familiar with the basic concepts, methods, techniques, and tools for the use of intelligent agents in computer-based systems. ❖ Understand the components and functions of intelligent agents. ❖ Apply the principles and methods of intelligent agents to a small- scale practical problem within the framework of a term project. ❖ Be prepared for further study in the design, implementation, and application of agent-based systems. ❖ Critically evaluate current trends in intelligent agents and their manifestation in business and industry.

11 © Franz J. Kurfess Objectives ❖ understand the notion of an agent  how agents are distinct from other software paradigms (e.g., objects),  characteristics of applications that lend themselves to an agent-oriented solution; ❖ understand the key issues associated with constructing agents  capable of intelligent autonomous action,  main approaches taken to developing such agents; ❖ understand the key issues and approaches to high-level communication  in multi-agent systems; ❖ understand the key issues in designing societies of agents  can effectively cooperate in order to solve problems; ❖ understand the main application areas of agent-based solutions; ❖ understand the main techniques for automated decision-making  in multi-agent systems  voting, forming coalitions, allocating scarce resources, and bargaining [Woolridge 2009]

12 © Franz J. Kurfess Evaluation Criteria

13 Trends in Computing Ubiquity Interconnection Intelligence Delegation Human-orientation

14 [Woolridge 2009] Ubiquity The continual reduction in cost of computing capability has made it possible to introduce processing power into places and devices that would have once been uneconomic As processing capability spreads, sophistication (and intelligence of a sort) becomes ubiquitous What could benefit from having a processor embedded in it…?

15 [Woolridge 2009] Interconnection Computer systems today no longer stand alone, but are networked into large distributed systems The internet is an obvious example, but networking is spreading its ever-growing tentacles… Since distributed and concurrent systems have become the norm, some researchers are putting forward theoretical models that portray computing as primarily a process of interaction

16 [Woolridge 2009] Intelligence The complexity of tasks that we are capable of automating and delegating to computers has grown steadily If you don’t feel comfortable with this definition of “intelligence”, it’s probably because you are a human

17 [Woolridge 2009] Delegation Computers are doing more for us – without our intervention We are giving control to computers, even in safety critical tasks One example: fly-by-wire aircraft, where the machine’s judgment may be trusted more than an experienced pilot Next on the agenda: fly-by-wire cars, intelligent braking systems, cruise control that maintains distance from car in front…

18 [Woolridge 2009] Human Orientation The movement away from machine-oriented views of programming toward concepts and metaphors that more closely reflect the way we ourselves understand the world Programmers (and users!) relate to the machine differently Programmers conceptualize and implement software in terms of higher-level – more human-oriented – abstractions

19 [Woolridge 2009] Programming progression… Programming has progressed through:  machine code;  assembly language;  machine-independent programming languages;  sub-routines;  procedures & functions;  abstract data types;  objects; to agents.

20 [Woolridge 2009] Global Computing What techniques might be needed to deal with systems composed of thousands of processors? Don’t be deterred by its seeming to be “science fiction” Hundreds of millions of people connected by once seemed to be “science fiction”… Let’s assume that current software development models can’t handle this…

21 [Woolridge 2009] Where does it bring us? Delegation and Intelligence imply the need to build computer systems that can act effectively on our behalf This implies:  The ability of computer systems to act independently  The ability of computer systems to act in a way that represents our best interests while interacting with other humans or systems

22 [Woolridge 2009] Interconnection and Distribution Interconnection and Distribution have become core motifs in Computer Science But Interconnection and Distribution, coupled with the need for systems to represent our best interests, implies systems that can cooperate and reach agreements (or even compete) with other systems that have different interests (much as we do with other people)

23 [Woolridge 2009] So Computer Science expands… These issues were not studied in Computer Science until recently All of these trends have led to the emergence of a new field in Computer Science:  intelligent agents, and  multi-agent systems

24 Definitions and Basic Concepts Agent Multi-Agent Systems

25 [Woolridge 2009] Definition: Agent An agent is  a computer system that is  capable of independent action on behalf of its user or owner (figuring out what needs to be done to satisfy design objectives, rather than constantly being told)

26 [Woolridge 2009] Definition: Multi-agent Systems A multi-agent system is one that consists of a number of agents, which interact with one another  In the most general case, agents will be acting on behalf of users with different goals and motivations  To successfully interact, they will require the ability to cooperate, coordinate, and negotiate with each other, much as people do

27 [Woolridge 2009] Agent Design, Society Design The course covers two key problems:  How do we build agents capable of independent, autonomous action, so that they can successfully carry out tasks we delegate to them?  How do we build agents that are capable of interacting (cooperating, coordinating, negotiating) with other agents in order to successfully carry out those delegated tasks, especially when the other agents cannot be assumed to share the same interests/goals? The first problem is agent design, the second is society design (micro/macro)

28 © Franz J. Kurfess Agent Design ❖ construction of individual agents ❖ capable of independent, autonomous action ❖ can successfully carry out tasks we delegate to them

29 © Franz J. Kurfess Agent Society Design ❖ design of collaborating agents ❖ capable of interacting with other agents  cooperation  coordination  negotiation ❖ successfully carry out delegated tasks  delegation may come from humans or other agents ❖ other agents cannot be assumed to share the same interests/goals

30 [Woolridge 2009] Multi-Agent Systems addresses questions such as:  emergence of cooperation in societies of self- interested agents  languages agents can use to communicate  recognition of conflicts among self-interested how can they (nevertheless) reach agreement  coordination of activities among autonomous agents to cooperatively achieve goals

31 [Woolridge 2009] Multi-Agent Systems these questions are all addressed in part by other disciplines  notably economics and social sciences, what makes this field unique:  emphasizes that the agents in question are computational, information processing entities

32 [Woolridge 2009] The Vision Thing It’s easiest to understand the field of multi-agent systems if you understand researchers’ vision of the future Fortunately, different researchers have different visions The amalgamation of these visions (and research directions, and methodologies, and interests, and…) define the field But the field’s researchers clearly have enough in common to consider each other’s work relevant to their own

33 Agent Examples NASA Deep Space 1 Air Traffic Control Autonomous Vehicles Internet Agents Personal Agents

34 [Woolridge 2009] Spacecraft Control 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 eventualities. This is costly and, if decisions are required quickly, it is simply not practicable. For these reasons, organizations like NASA are seriously investigating the possibility of making probes more autonomous — giving them richer decision making capabilities and responsibilities. This is not fiction: NASA’s DS1 has done it!

35 [Woolridge 2009] Deep Space 1 “Deep Space 1 launched from Cape Canaveral on October 24, During a highly successful primary mission, it tested 12 advanced, high-risk technologies in space. In an extremely successful extended mission, it encountered comet Borrelly and returned the best images and other science data ever from a comet. During its fully successful hyperextended mission, it conducted further technology tests. The spacecraft was retired on December 18, 2001.” NASA Web site

36 [Woolridge 2009] Autonomous Agents for specialized tasks DS1 example is one of a generic class  high-risk situations, unsuitable or impossible for humans varying degrees of autonomy  depending on the situation  remote human control may be an alternative, but not always

37 [Woolridge 2009] Air Traffic Control “A key air-traffic control system…suddenly fails, leaving flights in the vicinity of the airport with no air-traffic control support. Fortunately, autonomous air-traffic control systems in nearby airports recognize the failure of their peer, and cooperate to track and deal with all affected flights.” Systems taking the initiative when necessary Agents cooperating to solve problems beyond the capabilities of any individual agent

38 © Franz J. Kurfess Autonomous Airplanes ❖ current large airplanes have most of the technologies for autonomous flight  control of individual airplanes  coordination of activities with other airplanes relies on central authorities  requires dealing with non-autonomous airplanes  older or smaller ones  proposals have been put forward to allow more autonomy ❖ UAVs for military and security applications are in use  most of them are to a large degree remotely controlled

39 © Franz J. Kurfess Internet Agents ❖ tons of chat bots ❖ sales and marketing agents ❖ customer service

40 [Woolridge 2009] Internet Agents 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 pieces of information from various different Internet information sources. Failure would occur when a particular resource was unavailable, (perhaps due to network failure), or where results could not be obtained.

41 © Franz J. Kurfess IKEA’s Virtual Assistant Anna ❖ bin/ikea-caen.cgi bin/ikea-caen.cgi

42 © Franz J. Kurfess Apple’s Siri ❖ From Apple’s Siri FAQ:Siri FAQ  “Siri is the intelligent personal assistant that helps you get things done just by asking. It allows you to use your voice to send messages, schedule meetings, place phone calls, and more. But Siri isn’t like traditional voice recognition software that requires you to remember keywords and speak specific commands. Siri understands your natural speech, and it asks you questions if it needs more information to complete a task.” ❖ es/siri.html es/siri.html

43 [Woolridge 2009] What if the agents become better? Internet agents need not simply search They can plan, arrange, buy, negotiate – carry out arrangements of all sorts that would normally be done by their human user As more can be done electronically, software agents theoretically have more access to systems that affect the real-world But new research problems arise just as quickly…

44 Research Issues Human-Agent Interaction Agreements and Negotiation Scaling Issues Experiments Interdisciplinary Aspects

45 [Woolridge 2009] Research Issues Human-Agent Interaction  How do you state your personal preferences to your agent?  How can your agent compare different deals from different vendors?  What if there are many different parameters? Agreements and Negotiation  What algorithms can your agent use to negotiate with other agents (to make sure you get a good deal)? Scaling Issues  expanding agent use from individuals to large groups  automated procurement for corporations or government agencies Experiments  Robocup  Rescue Agent Competitions  Trading Agents Competition

46 [Woolridge 2009] Interdisciplinary Aspects The field of Multi-Agent Systems is influenced and inspired by many other fields:  Economics  Philosophy  Game Theory  Logic  Ecology  Social Sciences Strength  infusing well-founded methodologies into the field Weakness  there are many different views as to what the field is about Analogies with artificial intelligence itself

47 © Franz J. Kurfess Post-Test

48 © Franz J. Kurfess Evaluation ❖ Criteria

49 © Franz J. Kurfess Summary Introduction ❖ Intelligent Agents can be viewed as an evolutionary development in computer science  ubiquity, distributed systems, communication, collaboration  autonomy, delegation, human-centric orientation ❖ Multi-Agent Systems integrate possibly large numbers of autonomous agents  resource sharing, negotiation, collaboration, distributed problem solving ❖ Agents have been used in various application areas  space exploration, autonomous vehicles, personal assistants

50 © Franz J. Kurfess Important Concepts and Terms ❖ agent ❖ agent society ❖ agreement ❖ delegation ❖ environment ❖ intelligence ❖ multi-agent system ❖ negotiation ❖ resource sharing ❖ ubiquity

51 © Franz J. Kurfess