1. Do software agents know what they talk about?
Agents and Ontology
dr. Patrick De Causmaecker, Nottingham, March 2005

2. Reactive and hybrid agents

3. Agents and Ontology Patrick.DeCausmaecker@kahosl.be
Reactive
Symbolic representations and reactive manipulation do not work
Intelligent behaviour is linked to the environment in which the agent resides
Intelligent behaviour emerges from simple actions
Nottingham, March 2005
Agents and Ontology

4. The “subsumption” architecture (Brooks)
No explicit representations
No abstract reasoning
Intelligence emerges from complexity.
Nottingham, March 2005
Agents and Ontology

5. Agents and Ontology Patrick.DeCausmaecker@kahosl.be
The architecture
Two characteristics
Decisions through execution of tasks (behaviours as finite state machines
No symbolic representations or reasoning
Situation -> action
The different behaviours can fire simultanously
Hierarchy in priority layers
The see(…) is still present, but does no complex transformations on the sensorsignals
Nottingham, March 2005
Agents and Ontology

6. Agents and Ontology Patrick.DeCausmaecker@kahosl.be
Action selection
R: behaviour rules
<: priority relation
function action(p:P):A
var fired : (R)
var selected:A
begin
fired <-{(c,a)|(c,a) in R and p in c}
for each (c,a) in fired do
if there is no (c’,a’) < (c,a) in fired then
return a
end-if
end-for
return null
End function action
Nottingham, March 2005
Agents and Ontology

7. Agents and Ontology Patrick.DeCausmaecker@kahosl.be
Steels
Nottingham, March 2005
Agents and Ontology

8. Agents and Ontology Patrick.DeCausmaecker@kahosl.be
Nottingham, March 2005
Agents and Ontology

9. Luc Steels: the Mars explorer
Find stones on Mars. We do not know where the precious stones are. They do come in heaps. We have a number of autonomous vehicles that can drive round and pick up stones to bring them to the mother ship. There are obstacles making communication virtually impossible.
Nottingham, March 2005
Agents and Ontology

10. Agents and Ontology Patrick.DeCausmaecker@kahosl.be
Symbolic?
Luc Steels says this is completely unrealistic
His method:
Apply a gradient field that indicates the direction of the mother ship (radio signal)
Use radioactive crumbs that can be dropped, detected and picked up by the vehicles.
Nottingham, March 2005
Agents and Ontology

11. Agents and Ontology Patrick.DeCausmaecker@kahosl.be
The agents
Rules:
(1) if detect an obstacle then change direction
(2) if carrying samples and at the base then drop samples
(3) if carrying samples and not at the base then travel up gradient
(4) if detect a sample then pick sample up
(5) if true then move randomly
levels:
(1)<(2)<(3)<(4)<(5)
Nottingham, March 2005
Agents and Ontology

12. Agents and Ontology Patrick.DeCausmaecker@kahosl.be
Co-operation
rule 3 changes to 6:
(6) if carrying samples and not at the base then drop two crumbs and travel up gradient
rule 8 is added:
(8) if sense crumbs then pick up 1 crumb and travel down gradient
priority:
(1)<(2)<(6)<(4)<(8)<(5)
Nottingham, March 2005
Agents and Ontology

13. Agents and Ontology Patrick.DeCausmaecker@kahosl.be
Similar development
Patty Maes: Agent Network Architecture (ANA)
Nottingham, March 2005
Agents and Ontology

14. Limitations of reactive agents
The local environment must be rich.
How can non local information be modelled? The agents have a short term vision.
How can the agents learn?
How to design systems with emergent behaviour?
What if complexity requires many layers?
-> evolving agents, artificial life
Nottingham, March 2005
Agents and Ontology

15. Agents and Ontology Patrick.DeCausmaecker@kahosl.be
Hybrid agents
layer n
Input from perception …
Output action
layer 2
layer 1
layer n
layer n … …
layer 2
layer 2
layer 1
layer 1
Nottingham, March 2005
Agents and Ontology

16. Agents and Ontology Patrick.DeCausmaecker@kahosl.be
TouringMachines
Sensor input
Modelling layer
Perception subsystem
Action Subsystem
Planning layer
Reacive layer
Action output
Control subsystem
Nottingham, March 2005
Agents and Ontology

17. Agents and Ontology Patrick.DeCausmaecker@kahosl.be
The TouringMachine
Reactive layer:
E.g. avoid obstacles
Planning layer
Proactive behaviour using a library of schemata for plans
Modelling layer
Represents object and other agents in the system. Predicts conflicts and postulated objectives for the planning layer.
The control system decides which of the layers is active at a given moment.
Nottingham, March 2005
Agents and Ontology