Intelligent Agents Group @ RMIT
Prof. Lin Padgham (leader)
Ass. Prof. Michael Winikoff
Ass. Prof James Harland
Dr Lawrence Cavedon
Dr Sebastian Sardina
Dr John Thangarajah
4 Research assistants
12+ PhD students
www.cs.rmit.edu.au/agents
Agents and Agent Modelling
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In CS generally agreed agents are:
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Autonomous
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Reactive
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Proactive
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Situated in a (usually dynamic) environment
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Social (able to interact)
May or may not have explicit representations of
such things as goals, beliefs, plans, etc.
Different kinds of Agents
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Many different subfields within “agents”
One is the kind of ABM described by Peter
(SWARM style, many small simple agents)
often used for simulations and “emergent
behaviour”
Another is “belief, desire, intention” agents.
(modelled in terms of beliefs, goals, plans,
environmental events, etc.) These are the kind
of agents our “Intelligent Agents” group
specialises in.
Simple vs Complex Agents
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Emergent Intelligence: intelligent behaviour
emerges from many simple agents – e.g. ants.
Paradigm works well for some problems.
But breaks down when environment does not
provide all information needed for each step.
E.g. can program corridor following robot in this
way, but not a robot that can manage corners...
(episodic vs sequential)
Difficulty with long term complex goals.
Strengths of BDI Agents
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Natural way to model many systems.
Well developed paradigm with theoretical base and
implemented platforms.
Systems are very robust and flexible.
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flexible by different (sub) plans for different situations
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robust in that if one plan fails, system looks for another
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Very powerful for capturing complexity.
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Fast, suitable for real time applications.
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Widely used for defense department simulations.
Flexible and Robust
Get info
on
sustainability
Using book
Using the www
multiple plans
for how to
achieve my goal
From Peter
get book
read book
hierarchical structure:
Plans have subgoals,
and they also have
alternative plans...
from library
from shop
from friend
Each plan chosen
in context of current
situation.
If chosen plan fails
try another plan
for that goal...
Modular but Powerful
GOAL
Plan1
Each plan has
multiple steps
(sub-goals)
Plan2
Plan3
Multiple plans
Different ways
to achieve goal
Plan choice = 2
Subgoal steps = 3
Depth = 4
Over a million ways
to achieve the goal!!
Here we have 30 plans, 81 ways to achieve the goal.
Depends on choice of plans, number of steps, and depth
of tree.
Basic Architecture/Concepts
Beliefs
Percepts/
messages
Goals
Plans
Actions/
messages
BDI execution cycle
Beliefs &
plans
Chosen
plan
Event
Reasoning
about ordering
of intention
execution
Reasoning
about plan
to choose
Intention
stack
Action
Step
current
intention
Also smart failure recovery
Building Systems
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What are the agents
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What do they do (actions): effect on environment
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What info do they get (percepts): from environment
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Why do they do things (goals)
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Steps in doing more complex things (plans)
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Agents working together (communication,
coordination, teams, ...)
Example Uses of BDI Systems
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Systems acting (or advising) in dynamic
environments
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Simulations of complex agent systems
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e.g. air traffic control, meteorological alerting,
logistics, trading, tourism, ...
e.g. defense applications, games, training
simulators, disaster management
Personal helpers, WWW agents, etc.
Expertise in Our Group
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Agent Oriented Software Engineering
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methodologies and tools for building systems (we
are one of the world leaders here).
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Agent system design.
Additional agent reasoning integrated with basic
BDI
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planning, reasoning about conflicts, learning,
complex situations, probabilistic aspects.
Application Areas (past and present)
in our group...
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Meteorology
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High energy physics
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Tourism and travel
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Unmanned autonomous vehicles (UAVs)
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Interactive toys
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Roborescue, Robosoccer
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Games