Intelligent Agents Katia Sycara The E-Commerce Institute Teaching assistant: Joe Giampapa

Course Topics What are agents? What are multi-agent systems? Agent design and architecture Agents on the Desktop Agents in web-based info. management Agent interaction: –communication languages –coordination protocols –-agent interoperability

Course Topics (ctd) Infrastructure for finding Agent-based Services -Agent names servers - Middle Agents Agents in the marketplace -strategic behavior -mechanisms, negotiation, markets, auctions

Course Policies The course is based on lectures, lecture notes, and additional materials provided either electronically or in hard copy There will be no exams. Instead: Grading will be based on two projects – mid-term project (40%): a survey on a class-related topic development of an agent business case for agent technology in an area –bigger final project (60%)

Preface Agents are found in multiple applications: –information agents collect info. on behalf of users –financial agents monitor assets, perform transactions, help users negotiate –shopbots help finding best prices and deals –recommenders help with selecting shows/entertainment –multiple agents provide support in time-critical mission planning –multi-agent systems allow integration of previously stand-alone legacy applications

Example: Electronic Calendar

Is Electronic Calendar an Agent? It serves a user, it works on its behalf It is proactive: when a meeting is approaching, it alerts the user Is it autonomous? No. Its decisions on actions are user programmed, it does not reason and plan To be an intelligent agent, it needs to: –anticipate when the user does not need/want its action e.g., lookup vacation file, ask secretary –communicate with calendars to workout meetings –adapt to/learn user preferences

What Promotes Agent Proliferation Networked computing Distribution of expertise/resources Need for inter-operation between pre- existing isolated systems Need for personalization and customization The Internet: –enormous amount of available information –multiple service providers –e-commerce

What is Still Necessary? Support for secure transactions Micro-payments Standardized communication languages Ontologies Agreed-upon interaction protocols for trading, negotiation, etc For mobility: standard agent docking

But,What are Agents? A controversial issue. In this course we present several approaches

What is an Agent? A computational entity - but any program running on a machine is, too Intelligent - how exactly do we measure that? –is a program that can solve complex equations intelligent? –is a program that can find a good deal intelligent? Autonomous - the most agreed-upon attribute of agents, but not enough –means: decides for itself what it needs to do Collaborative - interacts with humans and others Adaptive

Calendar Example Your calendar will become an agent when: –it will collaborate with other: acquire relevant information from them, negotiate your meetings with them, etc –it will learn your preference and adapt to them: e.g., avoid meeting with Joe in the morning –change its action subject to info. on events: e.g., cancel outdoor class on a rainy day –notify you of selected events it finds on bboards

So, an Agent is… An autonomous, (preferably) intelligent, collaborative and adaptive computational entity Given some objectives/goals, an agent attempts to achieve them, without explicit instruction Here, intelligence is expressed in the ability to infer and execute the needed actions, and seek and incorporate relevant information, given the goals

Agents vs. Objects Objects, too, are autonomous computational entities. What is the difference? –agents are: usually persistent reactive, like objects, but also proactive may be self-aware have sole control over their actions –an object: has no say regarding the use and execution of its public methods. An agent may refuse or ask for compensation is not intelligent

Agents vs. Expert Systems Expert systems, common in the ‘80s: –provide advice to professionals in information intensive environments, e.g. advice for physicians in analyzing symptoms advice for car mechanics in repair –are “intelligent”, somewhat similar to agents, but –are reactive and not proactive –not autonomous - need instructions and intervention –do not interact with the environment or with other entities except for the user –usually not adaptive

Agent Attributes Delegation--performs tasks on users’ behalf Communication-- with user or other agents Autonomy--operates without direct user intervention Monitoring--environment so agent can act autonomously Actuation--affecting the environment Intelligence--interpret monitored events, reason

Evolution of Agents ‘80: Expert Systems: intelligence, expertise, server-like ‘80-’90: Objects: some autonomy re-use, interaction ‘90: Agents: personalization autonomy, intelligence, expertise, re-use, interaction, adaptation, persistence, proactivity Machine learning, human- computer interaction: adaptation, personalization Artificial intelligence, software engineering

Generic Agent Model Task Level Skills-e.g., information retrieval, filtering Knowledge ( a) a priori--developer, user or system specified (b) learned--dialog-based, case-based etc

Generic Agent Model Communication skills –with user--through interface –with other agents--through agent communication languages

Definition An agent is an autonomous computational entity, which: –is reactive and proactive –is goal driven –is intelligent: able to reason, plan and sometimes learn has domain specific intelligence –interacts with humans, other agents, and the environment via sensors and effectors in a high level language/protocol –anticipates user needs and reacts based on them –wish list: friendly, understands natural lang.,etc

End User Taxonomy of Agents Environment--e.g. desktop, Internet Task-Information gathering, negotiation Architecture--learning vs non-learning

Environment-based taxonomy Desktop agents: –operating system agents –application agents –application suite agents Internet Agents –search agents –information agents –notification agents

Environment-based taxonomy Intranet Agents –collaborative customization agents/workflow –business process automation agents –database agents

Features, Advantages and Benefits of Agents

Multi-Agent Systems (MAS) An agent is more useful in the context of others: –can concentrate on tasks of its expertise –can delegate other tasks to other experts –can take advantage of its ability to intelligently communicate, coordinate, negotiate But, a MAS is not just a collection of agents –it needs meaningful ways for agents to interact –it needs some system design and performance evaluation

Example: Calendar Multiple calendars interact with each other: –off load scheduling responsibility –interact with information agents that monitor for and filter information about events of interest –negotiate with other calendars

MAS - Two Approaches Build a system that is comprised of agents - should provide good performance Advantages may arise from: –possibility to develop each agent as an expert –incorporation of non-local expertise –rather simple to have multiple developers working concurrently Example: a system within an organization 1. Centralized design

MAS - Two Approaches Usually, the system has no prior static design, only single agents within Agents seek others to provide services, without knowing in advance who they are There is a need for agent finding mechanism Other agent may be non-cooperative or untrusted or malicious Example: markets, Internet 2. Open MAS