text: An information agent is viewed as a deductive database consisting of 3 parts: an observation database containing the facts the agent has observed or sensed from its surrounding environment, an input database containing the information the agent has obtained from other agents, and an intensional database which is a set of rules for computing derived information from the information stored in the observation and input databases. Stabilization of a system of information agents represents a capability of the agents to eventually get correct information about their surrounding despite unpredictable environment changes and the incapability of many agents to sense such changes causing them to have temporary incorrect information. We argue that the stabilization of a system of cooperative information agents could be understood as the convergence of the behavior of the whole system toward the behavior of a "superagent", who has the sensing and computing capabilities of all agents combined. We show that unfortunately, stabilization is not guaranteed in general, even if the agents are fully cooperative and do not hide any information from each other. We give sufficient conditions for stabilization and discuss the consequences of our results.
  @cite_9: This paper presents ALIAS, an agent architecture based on intelligent logic agents, where the main form of agent reasoning is abduction. The system is particularly suited for solving problems where knowledge is incomplete, where agents may need to make reasonable hypotheses about the problem domain and other agents, and where the raised hypotheses have to be consistent for the overall set of agents. ALIAS agents are pro-active, exhibiting a goal-directed behavior, and autonomous, since each one can solve problems using its own private knowledge base. ALIAS agents are also social, because they are able to interact with other agents, in order to cooperatively solve problems. The coordination mechanisms are modeled by means of LAILA, a logic-based language which allows to express intra-agent reasoning and inter-agent coordination. As an application, we show how LAILA can be used to implement inter-agent dialogues, e.g., for negotiation. In particular, LAILA is well-suited to coordinate the process of negotiation aimed at exchanging resources between agents, thus allowing them to execute the plans to achieve their goals.
  @cite_10: In multi-agent system, we often face incompleteness of information due to communication failure or other agent's suspension of decisions. To solve the incompleteness, we previously proposed speculative computation using abduction in the context of matter-slave multi-agent systems and gave a procedure in abductive logic programming [14]. In the work, a master agent prepares a default value for a question in advance and it performs speculative computation using the default without waiting for a reply for the question. This computation is effective unless the contradictory reply with the default is returned. However, we find that this mechanism is not sufficient for speculative computation in more general multi-agent systems such that replies can be revised according to other agents' speculative computation. In this paper, we formalize speculative computation with multi-agent belief revision and propose a correct procedure for such computation.
  @cite_12: 
summary: In this paper, we consider a specific class of cooperative information agents without considering effects of their actions on the environment e.g. in @cite_9 , @cite_12 , @cite_10 . We are currently working to extend the framework towards this generalized issue.