Transcript Agents-part1 - Dr Shahriar Bijani

ARTIFICIAL INTELLIGENCE
INTELLIGENT AGENTS
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Dr. Shahriar Bijani
Shahed University
Spring 2017
SLIDES’ REFERENCES
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Michael Rovatsos, Agent-Based Systems,
Edinburgh University, 2016.
S. Russell and P. Norvig, Artificial Intelligence:
A Modern Approach, Chapter 2, Prentice Hall,
2010, 3rd Edition.
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OUTLINE
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Introduction
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Agent & Multi-agent Systems
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Agents and environments
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Rationality
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PEAS
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Environment types
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Agent types
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NEW CHALLENGES FOR COMPUTER SYSTEMS
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Traditional design problem:
How can I build a system that produces the correct
output given some input?
Modern-day design problem:
How can I build a system that can operate
independently on my behalf in a networked,
distributed, large-scale environment in which it will
need to interact with different other components?
Distributed systems in which different components have
different goals and need to cooperate have not been
studied until recently
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WHAT IS AN AGENT? (1)
 An
agent is anything that can perceive its
environment through sensors and acting
upon that environment through actuators
(effectors)
Human agent: eyes, ears, and other organs for
sensors; hands, legs, mouth, and other body
parts for actuators
 Robotic agent: cameras and infrared range
finders for sensors; various motors for actuators
 The agent can only influence the environment but not
fully control it (sensor/actuator failure, nondeterminism)
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WHAT IS AN AGENT? (2)
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Definition from the agents community:
An agent is a computer system that is situated in
some environment, and that is capable of
autonomous action in this environment in order
to meet its design objectives
Adds a second dimension to the agent definition:
the relationship between agent and designer/user
Agent is capable of independent action
 Agent action is purposeful
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AGENT AUTONOMY
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Autonomy is a prerequisite for
Delegating (assigning) complex tasks to agents
 ensuring flexible action in unpredictable environments
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A system is autonomous:
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If it can learn and adapt
if its behavior is determined by its own experience
if it requires little help from the human user
if we don’t have to tell it what to do step by step
if it can choose its own goal and the way to achieve it
if we don’t understand its internal workings
Autonomy dilemma: how to make the agent smart
without losing control over it
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AGENTS AND ENVIRONMENTS
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The agent function maps from percept histories to
actions:
[f: P*  A]
The agent program runs on the physical architecture to
produce f
 agent = architecture + program
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VACUUM-CLEANER WORLD
Percepts: location and contents, e.g., [A,Dirty]
 Actions: Left, Right, Suck, NoOp
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MULTIAGENT SYSTEMS (MAS)
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Two fundamental ideas:
Individual agents are capable of autonomous
action to a certain extent (they don’t need to be told
exactly what to do)
 These agents interact with each other in multiagent
systems (and which may represent users with different
goals)
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Foundational problems of multiagent systems
research:
The agent design problem: how should agents act to
do their tasks?
 The society design problem: how should agents
interact to do their tasks?
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APPLICATIONS OF MULTI-AGENT SYSTEMS
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Two types of agent applications:
Distributed systems (processing nodes)
 Personal software assistants (aiding a user)
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Agents have been applied to various application areas:
Workflow/business process management
 Distributed sensing
 Information retrieval and management
 Electronic commerce
 Human-computer interfaces
 Virtual environments
 Social simulation
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RATIONAL AGENTS
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An agent should try to "do the right thing", based on
what it can perceive and the actions it can perform.
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The “right action” is the one that will cause the agent
to be most successful.
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Performance measure: An objective criterion for
success of an agent's behavior
 E.g., in a vacuum-cleaner agent: amount of dirt
cleaned up, amount of time taken, amount of
electricity consumed, amount of noise generated,
etc.
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RATIONAL AGENTS
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Rational Agent: For each possible percept sequence,
a rational agent should select an action that is
expected to maximize its performance measure,
given the evidence provided by the percept sequence
and whatever built-in knowledge the agent has.
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RATIONAL AGENTS
 Rationality
is distinct from omniscience
(knowing everything)
 Agents
can perform actions in order to
modify future percepts so as to obtain
useful information (information gathering,
exploration)
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WHAT IS AGENT TECHNOLOGY?
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Agents as a software engineering paradigm
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Interaction is most important aspect of complex software systems
Ideal for loosely coupled “black-box” components
Agents as a tool for understanding human societies
Human society is very complex, computer simulation can be
useful
 This has given rise to the field of (agent-based) social
simulation
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Agents vs. distributed systems
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Long tradition of distributed systems research
But MAS are not simply distributed systems, because of different
goals
Agents vs. economics/game theory
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Distributed rational decision making extensively studied in
economics, game theory very popular Many strengths but also
objections
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AGENTS VS AI
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Agents grew out of “distributed” AI
Much debate whether MAS is a sub-field of AI or vice versa
AI is mostly concerned with the building blocks of intelligence
 reasoning and problem-solving, planning and learning,
perception and action
The agents field is more concerned with
 Combining these components (this may mean we have to
solve all problems of AI, but agents can also be built
without any AI)
 Social interaction, which has mostly been ignored by
standard AI (and is an important part of human
intelligence)
Agents are a lot about integration (of abilities in one agent or
of agents in one environment)
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