@inproceedings{sarathy2021aamas, title={SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learning}, author={Vasanth Sarathy and Daniel Kasenberg and Shivam Goel and Jivko Sinapov and Matthias Scheutz}, booktitle={Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)}, year={2021}, synopsis = {We developed a new integrated planning and reinforcement learning framework called SPOTTER to enable agents to solve problems unsolvable from their initial planning domain.}, }

@InProceedings{law2020mate, author = {Theresa Law and Daniel Kasenberg and Matthias Scheutz}, year = {2020}, title = {Mate or weight? Perceptions of a robot as agent or object in a creative problem solving task}, booktitle = {Workshop on Creativity and Robotics: at {ICSR 2020}}, address = {Virtual}, month = {November}, topics = {hri}, projects = {social}, synopsis = {In this experiment we examine the extent to which this agency ascription can be overcome when required for completion of a task. In particular, we construct an “escape room”-like task, completion of which requires the participant to treat a robot as an object to be physically manipulated rather than an agent with which to be interacted. We compare conditions in which the robot is (minimally) interacting with the participant, inert, or entirely absent (replaced by another inanimate object). While the data are inconclusive, preliminary results are promising for the hypothesis that participants’ perception of the robot’s agency undermines their ability to complete the task..}, }

@inproceedings{kasenberg2020icaps, title={Generating Explanations for Temporal Logic Planner Decisions}, author={Daniel Kasenberg and Ravenna Thielstrom and Matthias Scheutz}, booktitle={Proceedings of the 30th International Conference on Automated Planning and Scheduling (ICAPS)}, year={2020}, topics={moral, comm}, projects={norm, mc}, },

@inproceedings{buckingham2020kr, title={Simultaneous Representation of Knowledge and Belief for Epistemic Planning with Belief Revision}, author={David Buckingham and Daniel Kasenberg and Matthias Scheutz}, booktitle={Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning (KR)}, year={2020}, projects={plan}, topics={hri, multi}, },

@InProceedings{kasenberg2019nl4xai, author={Daniel Kasenberg and Antonio Roque and Ravenna Thielstrom and Matthias Scheutz}, title={Engaging in Dialogue about an Agent's Norms and Behaviors}, booktitle={Proceedings of the 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI)}, year={2019}, topics={moral, comm}, projects={norm, mc, diarc}, synopsis={We present a set of capabilities allowing an agent planning with moral and social norms represented in temporal logic to respond to queries about its norms and behaviors in natural language, and for the human user to add and remove norms directly in natural language. The user may also pose hypothetical modifications to the agent's norms and inquire about their effects.}, }

@InProceedings{kasenberg2019inlg, author = {Daniel Kasenberg and Antonio Roque and Ravenna Thielstrom and Meia Chita-Tegmark and Matthias Scheutz}, title= {Generating Justifications for Norm-Related Agent Decisions}, booktitle={Proceedings of the 12th International Conference on Natural Language Generation}, year={2019}, topics={moral, comm}, projects={norm, mc}, synopsis={We present an approach to generating natural language justifications of decisions derived from norm-based reasoning. Assuming an agent which maximally satisfies a set of rules specified in an object-oriented temporal logic, the user can ask factual questions (about the agent's rules, actions, and the extent to which the agent violated the rules) as well as "why" questions that require the agent comparing actual behavior to counterfactual trajectories with respect to these rules. To produce natural-sounding explanations, we focus on the subproblem of producing natural language clauses from statements in a fragment of temporal logic, and then describe how to embed these clauses into explanatory sentences. We use a human judgment evaluation on a testbed task to compare our approach to variants in terms of intelligibility, mental model and perceived trust.}, }

@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}, year={2018}, 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.}, }