Wanlin Pang

Constraint reasoning over strings

This paper discusses an approach to representing and reasoning about constraints over strings. We discuss how string domains can often be concisely represented using regular languages, and how constraints over strings, and domain operations on sets of strings, can be carried out using this representation.

A constraint-based planner applied to data processing domains

Earth-science data processing at NASA is the problem of transforming low-level observations of the Earth system, such as data from Earth-observing satellites, into high-level observations or predictions, such as crop failure or high fire risk. Given the large number of socially and economically important variables that can be derived from the data, the complexity of the data processing needed to derive them and the many terabytes of data that must be processed each day, there are great challenges and opportunities in processing the data in a timely manner, and a need for more effective automation. Our approach to providing this automation is to cast it as a constraint-based planning problem: we represent data-processing operations as planner actions and desired data products as planner goals, and use a planner to generate data-flow programs that produce the requested data. The planning problem is translated into a constraint satisfaction problem (CSP) and solved by constraint propagation and search algorithms.

A graph based backtracking algorithm for solving general CSPs

Many AI tasks can be formalized as constraint satisfaction problems (CSPs), which involve finding values for variables subject to constraints. While solving a CSP is an NP-complete task in general, tractable classes of CSPs have been identified based on the structure of the underlying constraint graphs. Much effort has been spent on exploiting structural properties of the constraint graph to improve the efficiency of finding a solution. These efforts contributed to development of a class of CSP solving algorithms called decomposition algorithms. The strength of CSP decomposition is that its worst-case complexity depends on the structural properties of the constraint graph and is usually better than the worst-case complexity of search methods. Its practical application is limited, however, since it cannot be applied if the CSP is not decomposable. In this paper, we propose a graph based backtracking algorithm called ω-CDBT, which shares merits and overcomes the weaknesses of both decomposition and search approaches.