Clemens Mühlbacher

The Right Choice Matters! SMT Solving Substantially Improves Model-Based Debugging of Spreadsheets

Spreadsheets are undoubtedly the most prominent example of end-user programs vastly used in practice. Spreadsheets are often complex and comprise several hundreds or even thousands of formulas making fault localization a very hard and painstaking task. Although there has been work based on model-based reasoning for fault localization in spreadsheets, its applicability in practice is still limited mainly due to two reasons. First, most of the available constraint solvers, used for computing diagnoses, do not sufficiently support Real numbers. Second, the runtime especially for larger spreadsheets is too long preventing truly interactive debugging. In order to eliminate limitations regarding data type support and runtime performance, we suggest the use of SMT solvers for spreadsheet debugging. Besides lying out the foundations of SMT solving for diagnoses, we introduce its application to spreadsheet debugging. The empirical evaluation shows that the SMT solver Z3 requires six times less time for computing diagnoses compared to public available constraint solvers.

Improving dependability of industrial transport robots using model-based techniques

When autonomous robots are deployed in an industrial setting they are expected to work 24 hours a day, 7 days a week. Therefore, dependability of the robots is crucial. In this paper we present an approach following the model-driven engineering idea that supports dependability in different stages of the live cycle of robots. In particular we present how model-based testing and diagnosis can be used for this goal and how suitable models for these approaches can be obtained. The proposed approach was evaluated in a real industrial use-case showing superior performance compared to the hand-coded solutions used before.

Controlling Logistics Robots with the Action-Based Language YAGI

To achieve any meaningful tasks, a robot needs some form of task-level executive which acquires knowledge, reasons or plans, and performs and monitors actions. A formal approach for such agent programming is the Golog agent programming language. Golog is based on a first-order logic representation, and a drawback of common implementations is that in order to program agents, also knowledge of Prolog functionality is typically needed. In this paper, we present a prototype implementation of YAGI, a language rooted in Golog that offers a practical subset of the rich Golog framework in a more familiar syntax. Bridging imperative-style programming with an action-based specification, YAGI is more accessible to developers and provides a better ground for robot task-level executives. Moreover, we developed bindings for popular robotics frameworks such as ROS and Fawkes. As a proof of concept we present a YAGI-based agent for the RoboCup Logistics League which shows the expressiveness and the possibility to easily embed YAGI into robot applications.

Estimation of the Traversal Time for a Fleet of Industrial Transport Robots

Transportation tasks within warehouses are nowadays more and more solved using of fleets of autonomous robots. A fleet allows coordinating the different robots in such a way that it balance the load caused by transportation tasks. This allows the robot fleet to be a cost-efficient solution for moderate and changing loads compared to fixed conveyor belts.

Adapting Edge Weights for Optimal Paths in a Navigation Graph

Robots often use a topological graph to perform their navigation. To perform this navigation efficiently the traversal time along the edges of the graph needs to be properly estimated.

Supervision of Hardware, Software and Behavior of Autonomous Industrial Transport Robots

In this paper we motivate how model-based approaches can be used in all phases of the lifetime of an autonomous robot to improve the dependability. In particular we present preliminary results of a model-based runtime diagnosis and repair approach that is applied during operation of an industrial transport robot.

Diagnosis Makes the Difference for a Successful Execution of High-Level Robot Control Programs

Faults in action execution and perception that occur at runtime negatively affects the probability that an agent is able to finish a task successfully. There are several techniques such as hand-coded error recovery or diagnosis and repair approaches to deal with this problem. In this paper we present an experimental comparison of the fault tolerance of the popular (CRAM) framework using hand-coded error recovery and an approach using model-based diagnosis. The approaches were evaluated using a simulated robot delivery domain. Experimental results confirm that a control approach using diagnosis is able to significantly increase the success-rate and outperforms hand-coded error strategies.

Using Common Sense Invariants in Belief Management for Autonomous Agents

To design a truly autonomous robot it is necessary that the robot is able to handle unexpected action outcomes. One way to deal with these outcomes is to perform a diagnosis on the history of performed actions. Basically there are two ways to trigger such a diagnosis. One way to trigger it is a direct contradiction between a sensor reading and its expected value. Another way is to use background knowledge if an alternative action outcome does not lead to a direct contradiction. In order to avoid the necessity to hand-code this knowledge we propose to reuse a existing common sense knowledge base. In this paper we present an approach for reusing existing common sense knowledge in a belief management approach for autonomous agents.