New for: D1, D2, D3, D4, D5
This talk will also detail the issue of using
Causal Maps (CMs) at the higher knowledge-based
level of our architecture. Firstly, we will explain
how causal reasoning provides a foundation to 1) test a model
about the prediction of how agents will respond to (expected or not)
events; 2) explain how agents have done specific actions;
3) make a decision in a distributed environment; 4) analyze
and compare the agents' causal representations. All these aspects
are important for coordination, conflict solving and the emergence
of cooperation between agents.
Then, we will present a formal model for CMs with a precise
semantics based on relation algebra. Finally, we will explain
how to use this formal model by means of some examples.
We will finish this talk by sketching some aspects of our ongoing
research, particulary: (1) agents as logical systems;
(2) belief revision in multiagent environment and, (3) speech and
discursive acts.