6 research outputs found
AGM-Style Revision of Beliefs and Intentions from a Database Perspective (Preliminary Version)
We introduce a logic for temporal beliefs and intentions based on Shoham's
database perspective. We separate strong beliefs from weak beliefs. Strong
beliefs are independent from intentions, while weak beliefs are obtained by
adding intentions to strong beliefs and everything that follows from that. We
formalize coherence conditions on strong beliefs and intentions. We provide
AGM-style postulates for the revision of strong beliefs and intentions. We show
in a representation theorem that a revision operator satisfying our postulates
can be represented by a pre-order on interpretations of the beliefs, together
with a selection function for the intentions
Raffinement des intentions
Le résumé en français n'a pas été communiqué par l'auteur.Le résumé en anglais n'a pas été communiqué par l'auteur
Intention as Commitment toward Time
In this paper we address the interplay among intention, time, and belief in
dynamic environments. The first contribution is a logic for reasoning about
intention, time and belief, in which assumptions of intentions are represented
by preconditions of intended actions. Intentions and beliefs are coherent as
long as these assumptions are not violated, i.e. as long as intended actions
can be performed such that their preconditions hold as well. The second
contribution is the formalization of what-if scenarios: what happens with
intentions and beliefs if a new (possibly conflicting) intention is adopted, or
a new fact is learned? An agent is committed to its intended actions as long as
its belief-intention database is coherent. We conceptualize intention as
commitment toward time and we develop AGM-based postulates for the iterated
revision of belief-intention databases, and we prove a Katsuno-Mendelzon-style
representation theorem.Comment: 83 pages, 4 figures, Artificial Intelligence journal pre-prin
GROVE: A computationally grounded model for rational intention revision in BDI agents
A fundamental aspect of Belief-Desire-Intention (BDI) agents is intention revision. Agents revise their intentions in order to maintain consistency between their intentions and beliefs, and consistency between intentions. A rational agent must also account for the optimality of their intentions in the case of revision. To that end I present GROVE, a model of rational intention revision for BDI agents. The semantics of a GROVE agent is defined in terms of constraints and preferences on possible future executions of an agent’s plans. I show that GROVE is weakly rational in the sense of Grant et al. and imposes more constraints on executions than the operational semantics for goal lifecycles proposed by Harland et al. As it may not be computationally feasible to consider all possible future executions, I propose a bounded version of GROVE that samples the set of future executions, and state conditions under which bounded GROVE commits to a rational execution
GROVE: A computationally grounded model for rational intention revision in BDI agents
A fundamental aspect of Belief-Desire-Intention (BDI) agents is intention revision. Agents revise their intentions in order to maintain consistency between their intentions and beliefs, and consistency between intentions. A rational agent must also account for the optimality of their intentions in the case of revision. To that end I present GROVE, a model of rational intention revision for BDI agents. The semantics of a GROVE agent is defined in terms of constraints and preferences on possible future executions of an agent’s plans. I show that GROVE is weakly rational in the sense of Grant et al. and imposes more constraints on executions than the operational semantics for goal lifecycles proposed by Harland et al. As it may not be computationally feasible to consider all possible future executions, I propose a bounded version of GROVE that samples the set of future executions, and state conditions under which bounded GROVE commits to a rational execution