-
Chapter and Conference Paper
Formal Verification Based Synthesis for Behavior Trees
Behavior trees (BTs) have been extensively applied in the area of both computer games and robotics, as the control architectures. However, the construction of BTs is labor-expensive, time-consuming, and even i...
-
Chapter and Conference Paper
Collaborative Verification of Uninterpreted Programs
Given a set of uninterpreted programs to be verified, the trace abstraction-based verification method can be used to solve them once at a time. The verification of different programs is independent of each oth...
-
Chapter and Conference Paper
Trace Abstraction-Based Verification for Uninterpreted Programs
The verification of uninterpreted programs is undecidable in general. This paper proposes to employ counterexample-guided abstraction refinement (CEGAR) framework for verifying uninterpreted programs. Differen...
-
Chapter and Conference Paper
Using Coq for Formal Modeling and Verification of Timed Connectors
Formal modeling and verification of connectors in component-based software systems are getting more interest with recent advancements and evolution in modern software systems. In this paper, we use the proof a...
-
Chapter and Conference Paper
Reasoning About Connectors in Coq
Reo is a channel-based exogenous coordination model in which complex coordinators, called connectors, are compositionally built out of simpler ones. In this paper, we present a new approach to model connectors...
-
Chapter and Conference Paper
An Incremental Algorithm about the Affinity-Rule Based Transductive Learning Machine for Semi-Supervised Problem
One of the central problems in machine learning is how to effectively combine unlabelled and labelled data to infer the labels of unlabelled ones. In recent years, there has a growing interest on the transduct...
-
Chapter and Conference Paper
Transductive Learning Machine Based on the Affinity-Rule for Semi-supervised Problems and Its Algorithm
One of the central problems in machine learning is how to effectively combine unlabelled and labelled data to infer the labels of unlabelled ones. In this article, transductive learning machines are introduced...