Inger Klein

Planning in polynomial time: the SAS-PUBS class

This article describes a polynomial‐time, O(n3), planning algorithm for a limited class of planning problems. Compared to previous work on complexity of algorithms for knowledge‐based or logic‐based planning, our algorithm achieves computational tractability, but at the expense of only applying to a significantly more limited class of problems. Our algorithm is proven correct, and it always returns a parallel minimal plan if there is a plan at all.

A comparison of two methods for stochastic fault detection: the parity space approach and principal components analysis

This paper reviews and compares two methods for fault detection and isolation in a stochastic setting, assuming additive faults on input and output signals and stochastic unmeasurable disturbances. The first method is the parity space approach, analyzed in a stochastic setting. This leads to Kalman filter like residual generators, but with a FIR filter rather than an IIR filter as for the Kalman filter. The second method is to use principal component analysis (PCA). The advantage is that no model or structural information about the dynamic system is needed, in contrast to the parity space approach. We explain how PCA works in terms of parity space relations. The methods are illustrated on a simulation model of an F-16 aircraft, where six different faults are considered. The result is that PCA has similar fault detection and isolation capabilities as the stochastic parity space approach.

Efficient planning for a miniature assembly line

This paper presents a provably correct and efficient, polynomial time, planning tool and its application to a miniature assembly line for toy cars. Although somewhat limited, this process has many similarities with real industrial processes. One of our previous polynomial-time planning algorithms has been extended and adapted to work for a larger class of planning problems, including this particular process. The plans produced by the planner are then translated into GRAFCET charts, which are compiled into code for a programmable logic controller. Although capable of producing ordinary assembly plans, the system is mainly intended for producing plans in error recovery situations.

Automatic creation of sequential control schemes in polynomial time

Of all hard- and software developed for industrial control purposes, the majority is devoted to sequential, or binary valued, control and only a minor part to classical linear control. The sequential parts of the controller are typically invoked during startup or shut-down phases to bring the system either into its normal operating region or into some safe standby region. Despite its importance, fairly little theoretical research has been devoted to this area, and sequential control programs are still created manually without much support for a systematic approach. We propose a method to create sequential control programs automatically and online upon request, for example when a plant fault has occurred. The main idea is to spend some effort off-line on modeling the process, and from this model generate the control strategy, i.e. the plan. Here we present a planning tool implemented in a real-time expert system called G2. The planning system contains algorithms for creating plans in form of minimal GRAFCET charts that show maximal parallelism. The algorithms can handle a restricted class of problems and for this class the complexity only increases polynomially with the number of state variables.<<ETX>>

GLR tests for fault detection over sliding data windows

The Generalized Likelihood Ratio (GLR) test for fault detection as derived by Willsky and Jones is a recursive method to detect additive changes in linear systems in a Kalman filter framework. Here, we evaluate the GLR test on a sliding window and compare it to stochastic parity space approaches. Robust fault detection defined as being insensitive to faults in the signal space is also studied in the GLR framework.

On the planning problem in sequential control

The authors study a subclass of sequential control problems, the simplified action structures post-unique, unary, binary, single-valued (SAS-PUBS) class. They present a planning algorithm for this class. The algorithm is developed using a formalism from artificial intelligence. For planning problems in the SAS-PUBS class the algorithm finds a plan from a given initial state to a desired final state if and only if any plan exists solving the stated planning problem. Furthermore, the complexity of the given algorithm increases polynomially with the number of state variables. The proposed planning algorithm is applied to the problem of refueling an aircraft using a mobile refuel vehicle.<<ETX>>

Planning in Polynomial Time

This paper describes a polynomial-time, O(n3), planning algorithm for a limited class of planning problems. Compared to previous work on complexity of algorithms for knowledge-based or logic-based planning, our algorithm achieves computational tractability, but at the expense of only applying to a significantly more limited class of problems. Our algorithm is proven correct and complete, and it always returns a minimal plan if there is a plan at all.

Automatic synthesis of control programs in polynomial time for an assembly line

The industry wants provably correct and fast formal methods for handling combinatorial dynamical systems. One example of such problems is error recovery in industrial processes. We have used a provably correct, polynomial-time planning algorithm to plan for a miniature assembly line, which assembles toy cars. Although somewhat limited, this process has many similarities with real industrial processes. By exploring the structure of this assembly line we have extended a previously presented algorithm, thus extending the class of problems that can be handled in polynomial time. The planning tool presented here contains general-purpose algorithms that generate plans in the form of GRAFCET charts that are automatically translated into PLC code using a commercial PLC compiler.

The design of Sweden's first 5-year computer science and software engineering program

In 2013 Linköping University started the first 5-year engineering program in Computer Science and Software Engineering in Sweden. The goals of the program are to provide a holistic perspective on modern large scale software development, to provide a deep and broad understanding of computer science and computational thinking, and encourage innovation and entrepreneurship. The student response has been very good with more than 600 applicants to the 30 slots, of which more than 130 had this program as their first choice among all programs in Sweden. In this paper we present the goals, the design principles, and the resulting program. The ACM/IEEE CS Curricula has been used to make sure that the program provides a solid foundation in Computer Science. Three pedagogical ideas that we have used are (1) project courses to integrate theory and practice as well as provide experience with the most common form of working in industry; (2) courses that cover multiple programming paradigms and languages as well as multiple software development methodologies so that the students are prepared to take on the continual changes we know will come; and (3) a special course in engineering professionalism with groups of students from the first three years together reflecting on topics related to being a professional engineer. The paper concludes with a discussion about some important aspects such as computational thinking and the relation to the ACM/IEEE CS Curricula.

Fault isolation in discrete event systems by observational abstraction

We propose a method for fault isolation in discrete event systems such as object oriented control systems, where the observations are the logged error messages. The method is based on automatic abstraction that preserves only the behavior relevant to fault isolation. In this way we avoid the state space explosion, and a model checker can be used to reason about the temporal properties of the system. The result is a fault isolation table that maps possible error logs to isolated faults, and fault isolation thus reduces to table lookup. The fault isolation table can also be used as an analysis tool at the design level to find both faults that cannot be isolated as well as redundant error messages.