Current Research Projects:
l Design and Application of Software Agents : Logical Systems for Multi-Agents
In this project, we investigate agent-related logic systems. These systems can be mainly classified into two categories. One is the deductive logic and the other is the inductive one.
By deductive logics, we will use doxastic logics to express the agents‘ belief base, dynamic logic to model the cause-effect of agents’ actions, and preference theory to explain the motivation of agents‘ behavior and the consideration behind their decisions.
Moreover, we will consider the factors of time and agents‘ capability, to extend the expressive power of our logics. Though, for the sake of clarity and simplicity, we consider belief , preference, and action as the main concerns of agents in decision making. From these three components, we can further model the autonomy, intention and norms of agents. This will result in a basic architecture of agent logic systems, and we will call it BPA architecture.
As for the inductive logic, we will focus on the learning capability of agents. Since agents can induce the preference of user requirements during the course of execution, we need an inductive logic to model this phenomenon. In particular, agents should induce regularity and relevance from the data collected by them to form the basis of data classification so that the efficiency of information retrieval can be improved. These techniques of data mining will also be included in our logics。
Finally, we want to combine these logic systems to provide a semantic basis for agent description and communication languages and develop a symbolic reasoning language appropriate for programming agents.
l Logical Model for Privacy Protection:
In this project, we consider a logical model for privacy protection problem in the database linking context. Assume in the data center, there are a large amount of data records. Each record has some public attributes the values of which are known to the public and some confidential attributes the values of which are to be protected. When a data table is released, the data manager must assure that the receiver would not know the confidential data of any particular individuals by linking the releasing data and the prior information he had before receiving the data. In this project, we will investigate both the qualitative and quantitative aspects of the privacy protection problem.
To solve the problem from the qualitative aspect, we propose a simple epistemic logic to model the user’s knowledge. In the model, the concept of safety is rigorously defined and an effective approach is given to test the safety of the released data. Some generalization operations can be applied to the original data to make them less precise and the release of the generalized data may prevent the violation of privacy. Two kinds of generalization operations are considered. The level-based one is more restrictive, however, a bottom-up search method can be used to find the most informative data satisfying the safety requirement. On the other hand, the set-based one is more flexible, however, the computational complexity of searching through the whole spaces of this kinds of operations is much higher than the previous one though graph theory is used to simplify the discussion. As a result, heuristic methods may be needed to improve the efficiency.
While the level-based and set-based generalizations both replace a precise value by a subset of values, we can also consider the possibility of replacing it by a linguistic label or a fuzzy subset of values. This is called fuzzy generalizations. The logical model for the safety criteria by allowing fuzzy generalizations will also be investigated.
On the other hand, from the quantitative aspect, we will consider the information the user will obtain by receiving the data table. After receiving the data table, even the user can not know any individual’s privacy with certainty, he may learn the probability distribution of the confidential attribute values among a group of individuals and this may result in a danger of privacy invasion. To solve the problem, we will consider different information measures associated with data tables, such as Shannon’s entropy, Kolmogorov complexity, or the user’s posterior probing cost etc. These measures may provide aid in deciding how dangerous the release of the data table will be to the privacy protection policy.
l Information Fusion in Multi-Agent Systems:
In this project, we would like to develop logics for merging beliefs of agents with different degrees of reliability. The logics are obtained by combining the multi-agent epistemic logic from and multi-sources reasoning systems from. Every ordering for the reliability of the agents is represented by a modal operator, so we can reason with the merged results under different situations. The approach is conservative in the sense that if an agent's belief is in conflict with those of higher priorities, then his belief is completely discarded from the merged result. We consider two strategies for the conservative merging of beliefs. In the first one, if inconsistency occurs at some level, then all beliefs at the lower levels are discarded simultaneously, so it is called level cutting strategy. For the second one, only the level at which the inconsistency occurs is skipped, so it is called level skipping strategy. The formal semantics and axiomatic systems for these two strategies would be investigated.
l Information Retrieval by Possibilistic Reasoning
In this project, we would like to apply possibilistic reasoning to information retrieval for documents endowed with similarity relations. On the one hand, it is used together with some classical models for accommodating possibilistic uncertainty. The logical uncertainty principle is then interpreted in the possibilistic framework. On the other hand, possibilistic reasoning is integrated into terminological logic and its applications to some information retrieval problems, such as query relaxation, query restriction, and exemplar-based retrieval, would be investigated.