1. 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

2. Agents and Agent Modelling In CS generally agreed agents are:  Autonomous  Reactive  Proactive  Situated in a (usually dynamic) environment  Social (able to interact) May or may not have explicit representations of such things as goals, beliefs, plans, etc.

3. Different kinds of Agents 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.

4. Simple vs Complex Agents 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.

5. Strengths of BDI Agents Natural way to model many systems. Well developed paradigm with theoretical base and implemented platforms. Systems are very robust and flexible.  flexible by different (sub) plans for different situations  robust in that if one plan fails, system looks for another Very powerful for capturing complexity. Fast, suitable for real time applications. Widely used for defense department simulations.

6. Flexible and Robust Get info on sustainability Using book Using the www From Peter multiple plans for how to achieve my goal get 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... read book

7. Modular but Powerful GOAL Plan1Plan2Plan3 Multiple plans Different ways to achieve goal Each plan has multiple steps (sub-goals) Here we have 30 plans, 81 ways to achieve the goal. Depends on choice of plans, number of steps, and depth of tree. Plan choice = 2 Subgoal steps = 3 Depth = 4 Over a million ways to achieve the goal!!

8. Basic Architecture/Concepts Beliefs Goals Plans Percepts/ messages Actions/ messages

9. BDI execution cycle Beliefs & plans Chosenplan Intentionstack Event Action Stepcurrentintention Reasoning about ordering of intention execution Reasoning about plan to choose Also smart failure recovery

10. Building Systems What are the agents What do they do (actions): effect on environment What info do they get (percepts): from environment Why do they do things (goals) Steps in doing more complex things (plans) Agents working together (communication, coordination, teams,...)

11. Example Uses of BDI Systems Systems acting (or advising) in dynamic environments  e.g. air traffic control, meteorological alerting, logistics, trading, tourism,... Simulations of complex agent systems  e.g. defense applications, games, training simulators, disaster management Personal helpers, WWW agents, etc.

12. Expertise in Our Group Agent Oriented Software Engineering  methodologies and tools for building systems (we are one of the world leaders here).  Agent system design. Additional agent reasoning integrated with basic BDI  planning, reasoning about conflicts, learning, complex situations, probabilistic aspects.

13. Application Areas (past and present) in our group... Meteorology High energy physics Tourism and travel Unmanned autonomous vehicles (UAVs) Interactive toys Roborescue, Robosoccer Games