1. Evolving the goal priorities of autonomous agents
Adam Campbell
Advisor: Dr. Annie S. Wu
School of Electrical Engineering and Computer Science

2. Outline Motivation Genetic algorithms Agent control system
Experiments and results
Conclusion and future work

3. Motivation Imagine an insect with two goals
Get food
Avoid predator
When should the get food goal priority be higher than the avoid predator goal?
Action selection  How should agents choose between the actions of conflicting goals?
Two general methods
Take one action
Combine actions
Use a genetic algorithm to learn goal weights

4. Genetic algorithm Survival of fittest amongst problem solutions
General algorithm…
1) Initialize random population
2) Evaluate population
3) Select individuals
4) Recombine/mutate selected individuals
5) If stopping condition not satisfied
6) GOTO 2

5. Genetic algorithm example
Problem: find all black squares
Random population 4 3 5 2
Fitness

6. GA example continued Selected population Crossover & Mutation 5
Fitness 4 2 6

7. How is the GA used? Immediate goal functions
Produce a vector indicating where the agent should move in order to best satisfy the goal
Each immediate goal has a weight associated to it
Five immediate goal functions
Avoid agent
Avoid obstacle
Momentum
Go to area of interest (AOI)
Follow obstacle

8. Additional parameters
0.00
0.01
0.04
Randomness
Comfort
Allows obstacle following to occur

9. DEMO

10. Two scenarios

11. Average fitness Agents must survive and see as many AOIs as possible
Not much difference in fitness between two scenarios

12. Evolved parameters

13. Summary and conclusion
Discussed action selection problem in artificial intelligence and showed an evolutionary approach to solving
Tested approach on simple problem scenarios
Performed well on both scenarios
New behaviors (goals) can easily be added to the system
The parameters evolved are specific to the environment they were learned in

14. Future work Social interactions between agents
Allow communication of data between agents
New immediate goal functions needed
Allow agents to have more than one set of goal weights
Depending on the agent’s state (hungry, low on fuel, in danger, etc.) use a different set of goal weights
Other ways to combine vectors from immediate goal functions
Non-linear combination of vectors
Genetic programming
Currently being worked on at George Mason University
Better test scenarios
Evolve parameters that generalize well to unseen environments