In valid-time indeterminacy it is known that an event stored in a database did in fact occur, but it is not known exactly when. In this paper we extend the SQL data model and query language to support valid-time indeterminacy. We represent the occurrence time of an event with a set of possible instants, delimiting when the event might have occurred, and a probability distribution over that set. We also describe query language constructs to retrieve information in the presence of indeterminacy. These constructs enable users to specify their credibility in the underlying data and their plausibility in the relationships among that data. A denotational semantics for SQL’s select statement with optional credibility and plausibility constructs is given. We show that this semantics is reliable, in that it never produces incorrect information, is maximal, in that if it were extended to be more informative, the results may not be reliable, and reduces to the previous semantics when there is no indeterminacy. Although the extended data model and query language provide needed modeling capabilities, these extensions appear initially to carry a significant execution cost. A contribution of this paper is to demonstrate that our approach is useful and practical. An efficient representation of valid-time indeterminacy and efficient query processing algorithms are provided. The cost of

Recent growing interest in autonomous interacting software agents and their potential application in areas such as electronic commerce [Sandolm & Lesser 1995] has given increased importance to automated negotiation. MuchDAI and game theoretic research [Rosenschein & Zlotkin 1994; Osborne & Rubinstein 1994] deals with coordination and negotiation issues by giving pre-computed solutions to specific problems. There has been much research reported on developing theoretical models in which learning plays an eminent role, especially in the area of adaptive dynamics of games (e.g., [Jordan 1992; Kalai & Lehrer 1993]). However, to build autonomous agents that improve their negotiation competence based on learning from their interactions with other agents is still an emerging area. We are interested in developing autonomous agents capable of reasoning based on experience and improving their negotiation behavior incrementally. Learning in negotiation is closely coupled with...

We describe a meta-logic for characterizing the evolving internal reasoning of various families of agents. We view the reasoning of agents as ongoing processes rather than as fixed sets of conclusions. Our approach utilizes a strongly sorted calculus, distinguishing the application language, time, and various syntactic sorts. We have established soundness and completeness results corresponding to various families of agents. This allows for useful and intuitively natural characterizations of such agents' reasoning abilities. We discuss and contrast consistency issues as in the work of Montague and Thomason. We also show how to represent the concept of focus of attention in this framework. This material is based upon work supported by the National Science Foundation under Grant No. IIS9907482. We wish to thank the referees for their valuable comments and suggestions. 1 Keywords: logics of knowledge and beliefs, bounded agents, real-time reasoning, multiple agents. 1 Introduct...

Negotiation has been extensively discussed in gametheoretic, economic, and management science literatures for decades. Recent growing interest in electronic commerce has given increased importance to automated negotiation. Evidence both from theoretical analysis and from observations of human interactions suggests that if decision makers can somehow take into consideration what other agents are thinking and furthermore learn during their interactions how other agents behave, their payo# might increase. In this paper, we propose a sequential decision making model of negotiation, called Bazaar. Within the proposed negotiation framework, we model learning as a Bayesian belief update process. In this paper, we explore the hypothesis that learning is beneficial in sequential negotiation and present initial experimental results.

This paper develops a hybrid model which provides a unified framework for the fol- lowing four kinds of reasoning: 1) Zadeh's fuzzy approximate reasoning; 2) truthqualification uncertain reasoning with respect to fuzzy propositions; 3) fuzzy default reasoning (proposed, in this paper, as an extension of Reiter's default reasoning); and 4) truth-qualification uncertain default reasoning associated with fuzzy statements (developed in this paper to enrich fuzzy default reasoning with uncertain information). Our hybrid model has the following characteristics: 1) basic uncertainty is estimated in terms of words or phrases in natural language and basic propositions are fuzzy; 2) uncertainty, linguistically expressed, can be handled in default reasoning; and 3) the four kinds of rea- soning models mentioned above and their combination models will be the special cases of our hybrid model. Moreover, our model allows the reasoning to be performed in the case in which the information is fuzzy, uncertain and partial. More importantly, the problems of sharing the information among heterogeneous fuzzy, uncertain and default reasoning models can be solved efficiently by using our model. Given this, our framework can be used as a basis for information sharing and exchange in knowledge-based multi-agent systems for practical applications such as automated group negotiations. Actually, to build such a foundation is the motivation of this paper

The construction of rational agents is one of the goals that has been pursued in Artificial Intelligence (AI). In most of the architectures that have been proposed for this kind of agents, its behaviour is guided by its set of beliefs. In our work, rational agents are those systems that are permanently engaged in the process of rational inquiry; thus, their beliefs keep evolving in time, as a consequence of their internal inference procedures and their interaction with the environment. Both AI researchers and philosophers are interested in having a formal model of this process, and this is the main topic in our work. Beliefs have been formally modelled in the last decades using doxastic logics. The possible worlds model and its associated Kripke semantics provide an intuitive semantics for these logics, but they seem to commit us to model agents that are logically omniscient and perfect reasoners. We avoid these problems by replacing possible worlds by conceivable situations, which ar...

Abstract. We adopt the Markov chain framework to model bilateral negotiations among agents in dynamic environments and use Bayesian learning to enable them to learn an optimal strategy in incomplete information settings. Specifically, an agent learns the optimal strategy to play against an opponent whose strategy varies with time, assuming no prior information about its negotiation parameters. In so doing, we present a new framework for adaptive negotiation in such non-stationary environments and develop a novel learning algorithm, which is guaranteed to converge, that an agent can use to negotiate optimally over time. We have implemented our algorithm and shown that it converges quickly in a wide range of cases. 1