Understanding Agents in Multi-Agent Systems: Autonomous, Intelligent, and Interactive, Slides of Multiagent Systems

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LECTURE 2: INTELLIGENT

AGENTS

WHAT IS AN AGENT?

• The main point about agents is they are autonomous : capable of acting independently, exhibiting control over their internal state
• Thus: an agent is a computer system capable of autonomous action in some environment in order to meet its design objectives

2

SYSTEM

ENVIRONMENT

input output

REACTIVITY

• If a program‘s environment is guaranteed to be fixed, the program need never worry about its own success or failure – program just executes blindly - Example of fixed environment: compiler
• The real world is not like that: things change, information is incomplete. Many (most?) interesting environments are dynamic
• Software is hard to build for dynamic domains: program must take into account possibility of failure – ask itself whether it is worth executing!
• A reactive system is one that maintains an ongoing interaction with its environment, and responds to changes that occur in it (in time for the response to be useful)

PROACTIVENESS

• Reacting to an environment is easy (e.g., stimulus  response rules)
• But we generally want agents to do things for us
• Hence goal directed behavior
• Pro-activeness = generating and attempting to achieve goals; not driven solely by events; taking the initiative
• Recognizing opportunities

SOCIAL ABILITY

• The real world is a multi -agent environment:

we cannot go around attempting to achieve

goals without taking others into account

• Some goals can only be achieved with the

cooperation of others

• Similarly for many computer environments:

witness the Internet

• Social ability in agents is the ability to interact

with other agents (and possibly humans) via

some kind of agent-communication

language , and perhaps cooperate with others

OTHER PROPERTIES

• Other properties, sometimes discussed in the context of agency:
• mobility : the ability of an agent to move around an electronic network
• veracity : an agent will not knowingly communicate false information
• benevolence : agents do not have conflicting goals, and that every agent will therefore always try to do what is asked of it
• rationality : agent will act in order to achieve its goals, and will not act in such a way as to prevent its goals being achieved — at least insofar as its beliefs permit
• learning/adaption : agents improve performance over time

AGENTS AND OBJECTS

• Main differences:
  • agents are autonomous: agents embody stronger notion of autonomy than objects, and in particular, they decide for themselves whether or not to perform an action on request from another agent
  • agents are smart: capable of flexible (reactive, pro-active, social) behavior, and the standard object model has nothing to say about such types of behavior
  • agents are active: a multi-agent system is inherently multi-threaded, in that each agent is assumed to have at least one thread of active control

OBJECTS DO IT FOR FREE…

• agents do it because they want to
• agents do it for money

AGENTS AND EXPERT SYSTEMS

• Main differences:
  • agents situated in an environment: MYCIN is not aware of the world — only information obtained is by asking the user questions
  • agents act: MYCIN does not operate on patients
• Some real-time (typically process control) expert systems are agents

INTELLIGENT AGENTS AND AI

• Aren‘t agents just the AI project? Isn‘t building an agent what AI is all about?
• AI aims to build systems that can (ultimately) understand natural language, recognize and understand scenes, use common sense, think creatively, etc. — all of which are very hard
• So, don‘t we need to solve all of AI to build an agent…?

ENVIRONMENTS – ACCESSIBLE VS.

• An accessible environment is one in which the^ INACCESSIBLE agent can obtain complete, accurate, up-to- date information about the environment‘s state
• Most moderately complex environments (including, for example, the everyday physical world and the Internet) are inaccessible
• The more accessible an environment is, the simpler it is to build agents to operate in it

ENVIRONMENTS – DETERMINISTIC VS. NON- DETERMINISTIC

• A deterministic environment is one in which any action has a single guaranteed effect — there is no uncertainty about the state that will result from performing an action
• The physical world can to all intents and purposes be regarded as non-deterministic
• Non-deterministic environments present greater problems for the agent designer

ENVIRONMENTS - STATIC VS. DYNAMIC

• A static environment is one that can be assumed to remain unchanged except by the performance of actions by the agent
• A dynamic environment is one that has other processes operating on it, and which hence changes in ways beyond the agent‘s control
• Other processes can interfere with the agent‘s actions (as in concurrent systems theory)
• The physical world is a highly dynamic environment

ENVIRONMENTS – DISCRETE VS.

• An environment is discrete if there are a fixed, finite^ CONTINUOUS number of actions and percepts in it
• Russell and Norvig give a chess game as an example of a discrete environment, and taxi driving as an example of a continuous one
• Continuous environments have a certain level of mismatch with computer systems
• Discrete environments could in principle be handled by a kind of ―lookup table‖