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Salem Adra and Phil McMinn

Automated Discovery of Emergent Misbehaviour in Agent-Based Models. Salem Adra and Phil McMinn. Outline. Background: Agent-Based modelling Agent-Based modelling Applications Testing Agent-Based Models: Motivations and Challenges

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Salem Adra and Phil McMinn

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  1. Automated Discovery of Emergent Misbehaviour in Agent-Based Models Salem Adra and Phil McMinn ABM Workshop -Leeds

  2. Outline • Background: • Agent-Based modelling • Agent-Based modelling Applications • Testing Agent-Based Models: • Motivations and Challenges • Automated Discovery of Emergent Misbehaviour: Our Vision • Testing Strategies ABM Workshop -Leeds

  3. Background • What is Agent-Based computational modelling? • Increasingly popular modelling paradigm • Main Idea: • Simulates the interactions of autonomous entities (agents) with each other and their local environment to predict higher level emergent patterns • Bottom-up procedure • The focus goes into the individual interacting units • Benefits: • Deeper understanding of the interacting agents and their emergent behaviour ABM Workshop -Leeds

  4. Background • Example of some Agent-Based Modelling Frameworks: • FLAME (C Based) • MASON (Java Based) • RePast (Java Based) • Swarm (Objective C) ABM Workshop -Leeds

  5. Background Agent-Based Models Examples • The Epitheliome Project: • (Part of the Physiome Project, a worldwide public domain effort to provide a computational framework for understanding human physiology) • Goal: Modelling the Emergent Behaviour of Epithelial Cells • Benefits: • Better Understanding of Cell Proliferation and self-organisation • Capability to explore the roles of a single parameter in a complex biological system • Helps in the development of reconstruction methods for artificial human skin ABM Workshop -Leeds

  6. Background Agent-Based Models Examples Prediction and validation about Epithelial scratch wound healing process ABM Workshop -Leeds

  7. Background Agent-Based Models Examples Modelling of cardiac cells and tissues ABM Workshop -Leeds

  8. Background Agent-Based Models Examples Macroeconomic Policy Modelling ABM Workshop -Leeds

  9. Background Agent-Based Models Examples Social Insects: Pharaohs Ant ABM Workshop -Leeds

  10. Testing Agent-Based Models • Motivation: • ABM are increasingly used to make predictions that can affect organisations and even society • Hidden errors lurking in such models can be very costly • E.g. In the banking sector, losses made by NatWest, Barclays and Deutsche Morgan Grenfell totalling tens of millions of pounds were blamed on decisions that involved economic model errors • The correctness of such models and their thorough testing is vital • No Formal Testing Technique for ABM has yet been established ABM Workshop -Leeds

  11. Testing Agent-Based Models • Agents vs. Objects • “Object-oriented programmers often fail to see anything novel or new in the idea of agents. … While there are obvious similarities, there are also significant differences between agents and objects.” • N. R. Jennings, K. Sycara, and M. Wooldridge, “A roadmap • of agent research and development,” Journal of Autonomous • Agents and Multi-Agent Systems, vol. 1, no. 1, pp. 7–38, 1998. ABM Workshop -Leeds

  12. Testing Agent-Based Models • Agent vs. Objects: Evolution of Programming Approaches • Sources: • James J. Odell: "Objects and Agents Compared", in Journal of Object Technology, vol. 1, no. 1, May-June 2002, pp. 41-53. • Parunak, H. Van Dyke, "Go to the Ant": Engineering Principles from Natural Agent Systems, Annals of Operations Research, 75, 1997, pp. 69-101. ABM Workshop -Leeds

  13. Testing Agent-Based Models • Agents vs. Objects: • Main Differences: AUTONOMY and FLEXIBILITY Some examples of differences between Objects and Agents ABM Workshop -Leeds

  14. Testing Agent-Based Models • Challenges: • ABM are hard to test • Models often built to predict previously unseen emergent behaviour • Modellers themselves are not sure what to expect • Main purpose for modelling in the 1st place • Model Complexity • Simulating interactions of thousands and millions of agents is common • Tracing back certain model behaviour to certain agent or event is very hard ABM Workshop -Leeds

  15. Testing Agent-Based Models Errors and Artefacts in Agent-Based Modelling Galan et al. (2009), Journal of Artificial Societies and Social Simulation vol. 12, no. 1 ABM Workshop -Leeds

  16. Testing Agent-Based Models Without visual inspection this bug would have not been caught Most modelling work involve batch processing tasks (i.e. no visualisation) ABM Workshop -Leeds

  17. Testing Agent-Based Models • Some Suggested Informal Validation Solutions: • Application of an ABM to extreme scenarios • Re-implementing an ABM using: • different programming languages, • programming paradigms • agent-based modelling frameworks ABM Workshop -Leeds

  18. Testing Agent-Based Models Towards Formalising ABM testing The Misbehaviour Project: Project’s Aim: Automated Discovery of Emergent Misbehaviour (ADEM) Develop testing techniques and frameworks for agent-based software that can lead to the detection of systems’ Emergent Misbehaviour ABM Workshop -Leeds

  19. Testing Agent-Based Models Towards Formalising ABM testing Approach: Automated Testing Framework composed of 2 main steps Step 1- Learn and Build up an abstract model of the ABM to be tested Formulate fitness functions (simpler for task oriented ABMs) and constraints that define the model’s normal behaviour) Tools: Daikon (invariant detection), statistical and visual analysis methods Step 2- Search-Based Testing Process (using a black box approach) Search for scenarios, parameters or settings that might force the ABM to uncover “Misbehaviour” or bring it close to an extreme behaviour (i.e. violating a fitness functions and/or constraint) Tools: Metaheuristic techniques (e.g. GAs, Memetic Algorithms, … etc) ABM Workshop -Leeds

  20. Testing Agent-Based Models Start Misbehaviour Detected? Max Iterations Reached? No Yes No Yes Learn Model (DAIKON) No Bug Found Found Bug Produce new Behaviour (GAs) End ADEM Procedure ABM Workshop -Leeds

  21. Case Studies:Cancer Research and BT • 1 - Learn real model (i.e. invariants, agent’s properties and normal behaviour) • 2 - Produce buggy models out of the real models • How? • Mutation Testing: Introduction of artificial bugs • 3 – Apply ADEM Procedure: • Repeat until Misbehaviour Found or maximum iterations reached • IF any Misbehaviour already detected or any Invariant violated • Task accomplished • ELSE • Deploy metaheuristics to search for Misbehaviour • END ABM Workshop -Leeds

  22. Thank You. The End. ABM Workshop -Leeds

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