Intelligent Agents Group @ RMIT

1. 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 Intelligent Agents Group @ RMIT

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 multiple plans for how to achieve my goal Using book Using the www From Peter get book from library read book If chosen plan fails try another plan for that goal... from shop hierarchical structure: Plans have subgoals, and they also have alternative plans... from friend Each plan chosen in context of current situation.

7. Modular but Powerful GOAL Multiple plans Different ways to achieve goal Plan1 Plan2 Plan3 Each plan has multiple steps (sub-goals) 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.

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

9. BDI execution cycle Beliefs& plans Chosen plan Reasoning about ordering of intention execution Event Reasoning about plan to choose Intention stack Step current intention Action 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