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AI for Autonomous Ships – Challenges in Design and Validation

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AI for Autonomous Ships – Challenges in Design and Validation VTT TECHNICAL RESEARCH CENTRE OF FINLAND LTD AI for Autonomous Ships – Challenges in Design and Validation ISSAV 2018 Eetu Heikkilä Autonomous ships - activities in VTT . Autonomous ship systems . Unmanned engine room . Situation awareness . Autonomous autopilot . Connectivity . Human factors of remote and autonomous systems . Safety assessment 22/03/2018 2 Contents . AI technologies for autonomous shipping . Design & validation challenges . Methodological . Technical . Acknowledgements: Olli Saarela, Heli Helaakoski, Heikki Ailisto, Jussi Martio 22/03/2018 3 AI technologies Definitions - Autonomy Level Name 1 Human operated Autonomy is the ability of a system to 2 Human assisted achieve operational goals in complex domains by making decisions and executing 3 Human delegated actions on behalf of or in cooperation with 4 Supervised humans. (NFA, 2012) 5 Mixed initiative . Target to increase productivity, cost 6 Fully autonomous efficiency, and safety . Not only by reducing human work, but also by enabling new business logic 22/03/2018 5 Definitions - Artificial Intelligence . The Turing test: Can a person tell which of the other parties is a machine . AI is a moving target . When a computer program is able to perform a task, people start to consider the task “merely” computational, not requiring actual intelligence after all. ”AI is all the software we don’t yet know how to write.” . More practically: AI is a collection of technologies facilitating “smart” operation of machines and systems. Conclusions and actions fitting the prevailing situation. In many cases learning from data or experience. Picture: J.A. Sánchez Margallo, 22/03/2018 Wikipedia 6 It’s all about better utilization of data Monitor and report in real-time Predict for next best actions Optimize logistics, energy Information Integration Integrate analytics as a part of enterprise and raw materials utilisation IT systems and decision chains Expose new business opportunities Artificial intelligence methods for business purposes Easy-to-understand and descriptive visualization Application specific implementation of Intelligence Visualization of complex data algorithms and analysis Interactive methods for focusing Appropriate tools for domain specific relevant parts of the data applications Data Data fusion Collect past and real-time data Ensure reliable and relevant data acquisition and Acquire essential data from different sources from multiple sources and validation Manage high volumes of varying data storage 22/03/2018 7 Stages in AI development 1. Weak AI, Narrow AI . Focused on one narrow task, e.g., some game or diagnosis of a particular disease . Very limited adaptability, e.g., if the rules of a game are changed even slightly … . All current AI applications are Weak AI 2. Multi-agent systems . Interaction of several weak AI applications . The whole is larger than the sum of the parts . Being developed, e.g., autonomous vehicles, virtual assistants (Apple's Siri, Amazon Alexa, …) 3. Strong AI, General AI . Wide applicability and adaptability . Human-like consciousness . An evasive long-term research goal 4. Super AI . Machine intelligence exceeds human intelligence . Singularity: AI develops even more powerful AI . Machines might take over. Maybe some day (or some century) 22/03/2018 8 Machine learning . The bread and butter of the current AI boom, especially . Deep learning . Reinforcement learning Simple 풇 풙 . Supervised learning . Given 푥 and 푦 data, learn 푦 = 푓 푥 + 푒 Model identification Statistical pattern recognition . Unsupervised learning Artificial intelligence . Given 푥 data, discover patterns in it Complex 풇 풙 . Clustering, dimensionality reduction, anomaly detection, … 22/03/2018 9 Deep learning . Supervised learning with complex models . Especially large Artificial Neural Networks . Possibly millions of model parameters identified from data . Very good results in complex modelling . Nonlinear multivariate models . E.g., image classification . Downsides . Decisions cannot be well explained . Complex nonlinear models can behave strangely for some inputs 22/03/2018 Image from Jeff Clune: 10 Deep Learning Overview Reinforcement learning Agent . Determine an action based on balancing . Exploitation of previous good choices Action Observation . Exploration of possibilities not yet tried Environment . Observe the result from the action . Global optimization that builds a model with possibly a large number of parameters, e.g., a deep neural network. Requires a large number of iterations . Games . AlphaGo playing against itself . Consumer analytics . “You may also be interested in …" . Simulation models instead of real processes . Random trials on real processes might be dangerous . Validity of the simulator? 22/03/2018 11 Many more techniques are called AI … depending on task & model complexity … . Transfer learning . Adapt a large data set from a more-or-less similar task to supplement a small data set available from a new task. Reasoning . Rule-based systems, decision trees, case-based reasoning, … . Evolutionary computation . Genetic algorithms, … for challenging optimization tasks . Translation of natural languages . … 22/03/2018 12 Vision and natural language . Pictures with 200 categories, e.g. “ant” . Answering natural language questions based on pictures . Speech recognition from telephone calls 535.43 536.44 A: they think lunch is too long 536.67 537.28 B: {laugh} 537.33 541.56 A: so they're going to have like %uh thirty minutes for each period and they're going to extend the periods we're going to have more periods 542.24 543.15 B: oh God 22/03/2018 13 Y. Shoham et al: AI Index, November 2017 Uses for AI in autonomous ships . Situational awareness . Surroundings . Ship systems . Decision-making . Route planning . Navigational decisions 22/03/2018 14 Design & validation challenges Autonomy and AI vs. safety . Introduction of new technologies and ways of working brings along new and modified safety risks . Increasing system complexity . New interactions between humans and machines . Lack of prescriptive standards increases the technology developers’ responsibility for assuring safety 22/03/2018 16 Selected challenges in design & validation 22/03/2018 17 Concept design . Focus of development activities shifts towards the early concept design phase . Quality of system description . Including operating environment, stakeholders, interfaces . Concept of operations . Requirements management . Goals for the system performance 22/03/2018 18 Architecture & detailed design . Reliable handling of large data amounts needs to be ensured . Planning of data usage to teach the system 22/03/2018 19 Data quality issues are often realized only at a very late stage 22/03/2018 Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D. & Tufano, P. (2012). Analytics: 2020 The real-world use of big data. IBM Global Services. Implementation & integration . How to ensure the system learns the right things? . W-model . Increasing need for simulator testing . Transparency of machine learning Training 22/03/2018 21 Models can behave strangely for some inputs “Gibbon” “Panda” <1% distortion (99.3% confidence) + = . Distortions can be crafted to produce the desired erroneous outcome Example from https://www.darpa.mil/about-us/darpa-perspective-on-ai 22/03/2018 22 Verification & Validation . Lack of prescriptive standards . Technology developer increasingly responsible for demonstrating the safety . Goal-based approach used to link safety evidence & system requirements 22/03/2018 23 V&V methodology: Goal-based approach . Problem: How to create a comprehensible link between the safety goals and evidence? . System modeled as a structure of safety goals . GSN argumentationG0 modeling language Top Goal GOAL Is solved by Is solved by G1 G2 Goal Goal GOAL GOAL Is solved byIs solved by G1.1 G1.2 Sub-Goal Sub-Goal GOAL 22/03/2018 GOAL 24 Operation . Change management . New operational logic, increased human-machine interaction 22/03/2018 25 Conclusions Conclusions . AI technologies bring both opportunities and risks in the maritime sector . Robust process for V&V of AI systems is needed . Domain understanding needs to be incorporated in all stages of development 22/03/2018 27 TECHNOLOGY FOR BUSINESS Contact information: [email protected] +358 40 849 5790.
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