Forgetting/Oubli in areas (2025-06-09)

Agents use their own vocabularies to reason and talk about the world. Public signature awareness is satisfied if agents are aware of the vocabularies, or signatures, used by all agents they may, eventually, interact with. Multi-agent modal logics and in particular Dynamic Epistemic Logic rely on public signature awareness for modeling information flow in multi-agent systems. However, this assumption is not desirable for dynamic and open multi-agent systems because (1) it prevents agents to use unique signatures other agents are unaware of, (2) it prevents agents to openly extend their signatures when encountering new information, and (3) it requires that all future knowledge and beliefs of agents are bounded by the current state. We propose a new semantics for awareness that enables us to drop public signature awareness. This semantics is based on partial valuation functions and weakly reflexive relations. Dynamics for raising public and private awareness are then defined in such a way as to differentiate between becoming aware of a proposition and learning its truth value. With this, we show that knowledge and beliefs are not affected through the raising operations.

Public signature awareness is satisfied if agents are aware of the vocabulary, propositions, used by other agents to think and talk about the world. However, assuming that agents are fully aware of each other's signatures prevents them to adapt their vocabularies to newly gained information, from the environment or learned through agent communication. Therefore this is not realistic for open multi-agent systems. We propose a novel way to model awareness with partial valuations that drops public signature awareness and can model agent signature unawareness, and we give a first view on defining the dynamics of raising and forgetting awareness on this framework.

Some knowledge based systems will have to deal with increasing amount of knowledge. In order to avoid memory overflow, it is necessary to clean memory of useless data. Here is a first step toward an intelligent automatic forgetting scheme. The problem of the close relation between forgetting and inferring is addressed, and a general solution is proposed. It is implemented as invalidation operators for reasoning maintenance system dependency graphs. This results in a general architecture for selective forgetting which is presented in the framework of the Sachem system.

We propose a model of "agent" that has some characteristics at the crossroad of several ongoing research tracks: self rationality, autoepistemic reasoning, cooperative agents and resource-bounded reasoning. That model is particular since available technologies enable its implementation and thus its experimentation. Although in distributed artificial intelligence, the emphasis is on cooperation, we concentrate on belief management. We stress here the resource-bounded reasoning aspect of the work but describe first the architecture of our agents. We then describe the kind of behavior we expect from forgetting and show that this is achievable in both the theoretical and practical frameworks.