@InProceedings{kasenbergetal2018icres, author={Daniel Kasenberg and Vasanth Sarathy and Thomas Arnold and Matthias Scheutz and Tom Williams}, title={Quasi-Dilemmas for Artificial Moral Agents}, year={2018}, booktitle={International Conference on Robot Ethics and Standards}, synopsis={We describe moral quasi-dilemmas: situations similar to moral dilemmas, but in which an agent is unsure whether exploring the plan space or the world may reveal a course of action that satisfies all moral requirements. We argue that artificial moral agents should be built to handle MQDs, and that MQDs may be useful for evaluating AMA architectures.}, projects={mc, norm}, topics={ethics, moral}, image={}, video={}, }

@InProceedings{kasenbergscheutz18aaai, title = {Norm Conflict Resolution in Stochastic Domains}, author={Daniel Kasenberg and Matthias Scheutz}, booktitle={Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence}, year={2018}, topics={ethics,moral}, projects={norm,mc}, synopsis={We present an algorithm for artificial agents planning in Markov Decision Processes to maximally satisfy a set of potentially-conflicting norms (represented in Linear Temporal Logic).}, presentation={https://hrilab.tufts.edu/presentations/kasenbergscheutz18aaai.pdf}, }

@InProceedings{kasenberg2018hrip, title={Inferring and Obeying Norms in Temporal Logic}, author={Daniel Kasenberg}, booktitle={Proceedings of the Human-Robot Interaction (HRI) Pioneers Workshop}, year={2018}, topics={ethics,moral}, projects={norm,mc}, }

@InProceedings{kasenbergscheutz18aies, title={Inverse Norm Conflict Resolution}, author={Daniel Kasenberg and Matthias Scheutz}, booktitle={Proceedings of the 1st AAAI/ACM Workshop on Artificial Intelligence, Ethics, and Society}, year={2018}, topics={ethics,moral,learn}, projects={norm,mc}, synopsis={Given observed agent behavior in a stochastic environment and a set ofmoral/social norms represented in Linear Temporal Logic, we present an algorithm for computing a set of weights indicating the relative importance of those norms.}, }

@InProceedings{kasenbergetal18aies, title={Norms, Rewards, and the Intentional Stance: Comparing Machine Learning Approaches to Ethical Training}, booktitle={Proceedings of the 1st AAAI/ACM Workshop on Artificial Intelligence, Ethics, and Society}, year={2018}, author={Daniel Kasenberg and Thomas Arnold and Matthias Scheutz}, topics={ethics,moral,learn}, projects={norm,mc}, synopsis={We compare our norm inference approach with inverse reinforcement learning for the task of learning moral and social norms from demonstrators that either are governed by explicit temporal logic representations of norms, or are reward-maximizing. We argue that our approach can be thought of as analogous to Dennett's ``intentional stance''.}, }

@InProceedings{kasenberg18aiesdc, title={Learning and Obeying Conflicting Norms in Stochastic Domains}, booktitle={Proceedings of the Student Program, 1st AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society}, author={Daniel Kasenberg}, topics={ethics,moral,learn}, projects={norm,mc}, }

@InProceedings{kasenbergscheutz17cdc, author={Daniel Kasenberg and Matthias Scheutz}, title={Interpretable Apprenticeship Learning with Temporal Logic Specifications}, booktitle={Proceedings of the 56th IEEE Conference on Decision and Control (CDC 2017)}, year={2017}, projects={moral,ethics,learn}, topics={mc,norm}, synopsis={We consider the problem of inferring temporal logic specifications from agent behavior in stochastic domains. We formulate this as a multi-objective problem and use genetic programming to demonstrate the efficacy of our formluation.}, presentation={https://hrilab.tufts.edu/presentations/kasenbergscheutz17cdc.pdf}, }

@InProceedings{arnoldetal17aiethics, author = {Thomas Arnold and Daniel Kasenberg and Matthias Scheutz}, title = {Value Alignment or Misalignment -- What Will Keep Systems Accountable?}, booktitle = {{AAAI} Workshop on {AI}, Ethics, and Society}, year = {2017}, projects={norm,mc,plan}, topics={ethics,hri,moral,prag}, synopsis={We present here a critique of a turn toward inverse reinforcement-learning as a way to guarantee ethical AI systems. We argue that a hybrid architecture, which can represent norms explicitly, is necessary for basic social coordination and moral reasoning between people and AI systems.}, }