Research on Qualitative Model Decomposition

Qualitative reasoning uses incomplete knowledge to compute a description of the possible behaviors for dynamic systems. A standard qualitative simulation（QSIM） algorithm frequently results in a large number of incomprehensible behavioral descriptions and the simulation for complex systems frequently is intractable. Two model de- composition methods are proposed in this paper to eliminate or decrease the insujficiency of this algorithm. Using a directed graph to represent the qualitative model, the strongly connected graph based theory and genetic algorithm based model decomposition are proposed to decompose the model. A new simple system model is reconstructed by subgraphs and causal relations when the system directed graph is decomposed completely. Each sub-graph is viewed as a separate system and will be simulated separately, and the simulation result of causally upstream subsystem is used to constrain the behavior of downstream subsystems. The model decomposition algorithm provides a promising paradigm for qualitative simulation whose complexity is driven by the complexity of the problem specification rather than the inference mechanism used.

Knowledge Map： Mathematical Model and Dynamic Behaviors

Knowledge representation and reasoning is a key issue of the Knowledge Grid. This paper proposes a Knowledge Map (KM) model for representing and reasoning causal knowledge as an overlay in the Knowledge Grid. It extends Fuzzy Cognitive, Maps (FCMs) to represent and reason not only simple cause-effect relations, but also time-delay causal relations, conditional probabilistic causal relations and sequential relations. The mathematical model and dynamic behaviors of KM are presented. Experiments show that, under certain conditions, the dynamic behaviors of KM can translate between different states. Knowing this condition, experts can control or modify the constructed KM while its dynamic behaviors do not accord with their expectation. Simulations and applications show that KM is more powerful and natural than FCM in emulating real world.

A formal model of fair exchange protocols

Based on the study of existing fair exchange protocols, this paper sets up an accurate formal model by stepwise refinement. In the process of refinement an unreliable channel is employed to simulate an attack behavior. The model provides a novel formal definition of exchanged items, and presents the formal goals for fairness, accountability, etc., reflecting the inherent requirements for fair exchange protocols across-the-board. In order to check, prove, and design fair exchange protocols effectively and efficiently, the model puts forward a novel property of abuse-freeness which applies to all fair exchange protocols, gives a formal definition for trust strand of the third party, and presents general criteria of designing a secure and effective fair exchange protocol. Taking a typical fair exchange protocol as an example, this paper presents the analysis steps of fair exchange protocols appealing to our model. An unknown attack is uncovered. The analysis reveals the process of a complete attack, discovering deeper reasons for causing an attack. Finally, we modify the flawed protocol and the revised protocol ensures the desirable properties.