Adaptive Constraint-Based Agents in Artificial Environments

[AGENTS]   [Reactive Agents]   [Triggering Agents]   [Deliberative Agents]   [Hybrid Agents]   [Anytime Agents]

[ Please note: The project has been discontinued as of May 31, 2005 and is superseded by the projects of the ii Labs. There won't be further updates to these pages. ]

Triggering Agents

(Related publications: [PUBLink] [PUBLink])

Triggering agents introduce internal states. Past information can thus be utilized by the rules, and sequences of actions can be executed to attain longer-term goals. A possible rule might look like this:

IF (distribution_mode) AND (leading_helicopter == left) THEN

Popular Alife agent systems like CyberLife's Creatures [PUBLink], P.F. Magic's Virtual Petz [PUBLink] and Brooks' subsumption architecture [PUBLink] are examples of this category. Indeed, nearly all of today's computer games apply this approach, using finite state machines to implement it.

These agents can react as fast as reactive agents and also have the ability to attain longer-term goals. But they are still based on hard-wired rules and cannot react appropriately to situations that were not foreseen by the programmers or have not been previously learned by the agents (e.g., by neural networks).

[AGENTS]   [Reactive Agents]   [Triggering Agents]   [Deliberative Agents]   [Hybrid Agents]   [Anytime Agents]

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Last update:
May 19, 2001 by Alexander Nareyek