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Projects in KPLAB

Research in KPLAB takes place in a number of projects, which are often run in close cooperation with excellence centers and research environments such as MOVIII, CADICS and LinkLab. The following projects (listed in alphabetical order) are actively being pursued.

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Logical Agents

A dialog-based adventure game using Logical Agents.

[Knowledge Processing Lab]

The Logical Agents project builds an agent architecture based on automated reasoning in TAL. Each agent is equipped with a knowledge base, a database of logical formulas encoding background knowledge as well as memory. Automated theorem proving is used for reasoning with the content of the knowledge base, answering questions, and planning actions. Issues investigated in this project include the use of incompletely specified action timing, the generation of plans with infinite loops, and the integration of planning, execution, and plan revision.

» Read more about Logical Agents!

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Reconfigurable Diagnosis Systems: FlexDx

An overview of the components of the FlexDx implementation.

[Knowledge Processing Lab]

Detecting and isolating multiple faults is a computationally intense task which typically consists of computing a set of tests, and then computing the diagnoses based on the test results. FlexDx is a reconfigurable diagnosis framework that reduces the computational burden by only running the tests that are currently needed. The method selects tests such that the isolation performance of the diagnostic system is maintained. The DyKnow knowledge processing middleware framework plays an important role by handling issues related to the dynamic addition and removal of tests, the ability to perform tests on historic data, and the combination of synchronous and asynchronous processing.

» Read more about FlexDx!

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Troubleshooting of Automotive Systems

Heavy trucks – an application domain for troubleshooting.

[Knowledge Processing Lab, Scania AB]

In the Troubleshooting of Automotive Systems project, we develop methods for efficient automatic troubleshooting of complex systems such as heavy trucks. Troubleshooting software should guide mechanics in a workshop in the process of finding and repairing vehicle faults, with the aim being to minimize the expected cost in terms of time and money. This problem can be modeled as a probabilistic planning problem where the vehicle to be repaired is a partially observable system. This project is run in close collaboration with the truck manufacturer Scania CV AB.

» Read more about Troubleshooting of Automotive Systems!

Collaborative Robotic Systems

[Knowledge Processing Lab, UASTechnologies Lab]

As robotic systems begin to enter society and take on tasks that involve cooperation and collaboration with humans, there are a great many research challenges that need to be tackled before robots and humans can operate together in a robust, reliable, resilient and safe manner. The focus of the following project area in AIICS covers a broader set of topics essential to the realization of this goal. The primary flavor of this area of research combines artificial intelligence techniques with traditional robotics technologies. This is a research trend that is becoming increasingly important and will only increase in importance in the coming decades. Consequently, topics associated with collaborative robotics are well-placed to have great impact in contributing to viable solutions for many of the scientific and technical challenges that lie ahead of us. Some of the specific topics of interest are: mixed-initiative interaction, human-aware robotics, task delegation frameworks, distributed planning, distributed information structures, cloud robotics and human-robot interaction.

» Read more about this project area and our activities!

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Previous projects in KPLAB


Many forms of deliberation used by autonomous agents, including prediction and planning, make use of formal models of the environment in which the agent operates. In many cases, a tradeoff must be made between accuracy and performance, where more detailed models may provide highly accurate results but are not always feasible given limited computational resources. In most cases, this tradeoff is fixed by humans when a system is designed. The DARE project – Dynamic Abstraction-driven Replanning and Execution – investigates methods where agents take over part of this responsibility in the context of interleaved task planning and execution for fully or partially observable stochastic domains, dynamically constructing planning models at varying levels of abstraction as required by the problem at hand.


The Heuristic Planning and Prediction Project (HPAP) revolved around the development of a set of domain-independent heuristics for automated planning.

» Read more about the HPAP project!


A plan generated by PARADOCS and its associated temporal constraint network.

PARADOCS is a Prolog implementation of a reasoning system for a subset of TAL, that views Planning And Reasoning As DeductiOn with ConstraintS. It supports prediction from a fully instantiated set of actions, planning from the empty set of actions, and anything in between.

PARADOCS has been superseded by the Logical Agents project, which uses many of the insights gained through the development of PARADOCS.


The Yamaha RMAX was the main platform for the WITAS project.

The WITAS Unmanned Aerial Vehicle project (1997-2005) had the long term goal of designing and deploying intelligent autonomous aircraft. Though the WITAS project itself has finished, many of the research topics are still actively being pursued withing other projects. (Groups: KPLAB, CASL, SCML)

» Read more about the WITAS project!

Page responsible: Patrick Doherty
Last updated: 2014-04-30