Introduction-Artificial Intelligence-Lecture Slides, Slides of Artificial Intelligence

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LECTURE 1: INTRODUCTION docsity.com Overview  Five ongoing trends have marked the history of computing:  ubiquity;  interconnection;  intelligence;  delegation; and  human-orientation docsity.com 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 docsity.com 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… docsity.com 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 docsity.com 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 docsity.com 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) docsity.com 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: multiagent systems docsity.com 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) docsity.com Multiagent Systems  In Multiagent Systems, we address questions such as:  How can cooperation emerge in societies of self- interested agents?  What kinds of languages can agents use to communicate?  How can self-interested agents recognize conflict, and how can they (nevertheless) reach agreement?  How can autonomous agents coordinate their activities so as to cooperatively achieve goals? docsity.com Multiagent Systems  While these questions are all addressed in part by other disciplines (notably economics and social sciences), what makes the multiagent systems field unique is that it emphasizes that the agents in question are computational, information processing entities. docsity.com Deep Space 1  http://nmp.jpl.nasa.gov/ds1/  ―Deep Space 1 launched from Cape Canaveral on October 24, 1998. 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 docsity.com Autonomous Agents for specialized tasks  The DS1 example is one of a generic class  Agents (and their physical instantiation in robots) have a role to play in high-risk situations, unsuitable or impossible for humans  The degree of autonomy will differ depending on the situation (remote human control may be an alternative, but not always) docsity.com 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 docsity.com Research Issues  How do you state your preferences to your agent?  How can your agent compare different deals from different vendors? What if there are many different parameters?  What algorithms can your agent use to negotiate with other agents (to make sure you get a good deal)?  These issues aren’t frivolous – automated procurement could be used massively by (for example) government agencies  The Trading Agents Competition… docsity.com Multiagent Systems is Interdisciplinary  The field of Multiagent Systems is influenced and inspired by many other fields:  Economics  Philosophy  Game Theory  Logic  Ecology  Social Sciences  This can be both a strength (infusing well-founded methodologies into the field) and a weakness (there are many different views as to what the field is about)  This has analogies with artificial intelligence itself docsity.com Some Views of the Field  Agents as a paradigm for software engineering: Software engineers have derived a progressively better understanding of the characteristics of complexity in software. It is now widely recognized that interaction is probably the most important single characteristic of complex software  Over the last two decades, a major Computer Science research topic has been the development of tools and techniques to model, understand, and implement systems in which interaction is the norm docsity.com Objections to MAS  Isn’t it all just Distributed/Concurrent Systems? There is much to learn from this community, but:  Agents are assumed to be autonomous, capable of making independent decision – so they need mechanisms to synchronize and coordinate their activities at run time  Agents are (can be) self-interested, so their interactions are ―economic‖ encounters docsity.com Objections to MAS  Isn’t it all just AI?  We don’t need to solve all the problems of artificial intelligence (i.e., all the components of intelligence) in order to build really useful agents  Classical AI ignored social aspects of agency. These are important parts of intelligent activity in real-world settings docsity.com Objections to MAS  Isn’t it all just Economics/Game Theory? These fields also have a lot to teach us in multiagent systems, but:  Insofar as game theory provides descriptive concepts, it doesn’t always tell us how to compute solutions; we’re concerned with computational, resource-bounded agents  Some assumptions in economics/game theory (such as a rational agent) may not be valid or useful in building artificial agents docsity.com