Intelligent Agents

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Intelligent Agents

• Katia Sycara
• The E-Commerce Institute
• katia_at_cs.cmu.edu
• www.cs.cmu.edu/softagents
• Teaching assistant Joe Giampapa
• garof_at_cs.cmu.edu

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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

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Course Topics (ctd)

• Infrastructure for finding Agent-based Services
• -Agent names servers
• - Middle Agents
• Agents in the marketplace
• -strategic behavior
• -mechanisms, negotiation, markets, auctions

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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)

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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

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Example Electronic Calendar
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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

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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

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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

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But,What are Agents?

• A controversial issue.
• In this course we present several approaches

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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

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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

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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

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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

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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

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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

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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 ad
aptation, personalization
Artificial intelligence, software engineering
?
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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

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Generic Agent Model

• Communication skills
• with user--through interface
• with other agents--through agent communication
  languages

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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

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End User Taxonomy of Agents

• Environment--e.g. desktop, Internet
• Task-Information gathering, negotiation
• Architecture--learning vs non-learning

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Environment-based taxonomy

• Desktop agents
• operating system agents
• application agents
• application suite agents
• Internet Agents
• search agents
• information agents
• notification agents

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Environment-based taxonomy

• Intranet Agents
• collaborative customization agents/workflow
• business process automation agents
• database agents

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Features, Advantages and Benefits of Agents
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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

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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

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MAS - Two Approaches
1. Centralized design

• 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

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MAS - Two Approaches
2. Open MAS

• 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