Explicability? Legibility? Predictability? Transparency? Privacy? Security? The Emerging Landscape of Interpretable Agent Behavior

There has been significant interest of late in generating behavior of agents that is interpretable to the human (observer) in the loop. However, the work in this area has typically lacked coherence on the topic, with proposed solutions for "explicable", "legible", "predictable" and "transparent" planning with overlapping, and sometimes conflicting, semantics all aimed at some notion of understanding what intentions the observer will ascribe to an agent by observing its behavior. This is also true for the recent works on "security" and "privacy" of plans which are also trying to answer the same question, but from the opposite point of view -- i.e. when the agent is trying to hide instead of revealing its intentions. This paper attempts to provide a workable taxonomy of relevant concepts in this exciting and emerging field of inquiry.

[1]  Erez Karpas,et al.  Goal Recognition Design , 2014, ICAPS.

[2]  Subbarao Kambhampati,et al.  Balancing Explicability and Explanation in Human-Aware Planning , 2017, AAAI Fall Symposia.

[3]  Subbarao Kambhampati,et al.  Projection-Aware Task Planning and Execution for Human-in-the-Loop Operation of Robots in a Mixed-Reality Workspace , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[4]  Anca D. Dragan,et al.  On the Utility of Model Learning in HRI , 2019, 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[5]  Ronen I. Brafman,et al.  A Privacy Preserving Algorithm for Multi-Agent Planning and Search , 2015, IJCAI.

[6]  Hadas Kress-Gazit,et al.  Sorry Dave, I'm Afraid I Can't Do That: Explaining Unachievable Robot Tasks Using Natural Language , 2013, Robotics: Science and Systems.

[7]  Hadas Kress-Gazit,et al.  Towards minimal explanations of unsynthesizability for high-level robot behaviors , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Sebastian Sardiña,et al.  Deceptive Path-Planning , 2017, IJCAI.

[9]  Subbarao Kambhampati,et al.  Human-Aware Planning Revisited : A Tale of Three Models , 2018 .

[10]  Subbarao Kambhampati,et al.  A Candidate Set Based Analysis of Subgoal Interactions in Conjunctive Goal Planning , 1996, AIPS.

[11]  Blai Bonet,et al.  A Concise Introduction to Models and Methods for Automated Planning , 2013, A Concise Introduction to Models and Methods for Automated Planning.

[12]  Yu Zhang,et al.  Interactive Plan Explicability in Human-Robot Teaming , 2018, 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN).

[13]  Yu Zhang,et al.  Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy , 2017, IJCAI.

[14]  Subbarao Kambhampati,et al.  Planning with Explanatory Actions: A Joint Approach to Plan Explicability and Explanations in Human-Aware Planning , 2019, ArXiv.

[15]  Yehuda Lindell,et al.  Secure Multiparty Computation for Privacy-Preserving Data Mining , 2009, IACR Cryptol. ePrint Arch..

[16]  Matthias Scheutz,et al.  "Sorry, I Can't Do That": Developing Mechanisms to Appropriately Reject Directives in Human-Robot Interactions , 2015, AAAI Fall Symposia.

[17]  Siddhartha S. Srinivasa,et al.  Effects of Robot Motion on Human-Robot Collaboration , 2015, 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[18]  Anca D. Dragan,et al.  Generating Plans that Predict Themselves , 2018, WAFR.

[19]  Siddhartha S. Srinivasa,et al.  Legibility and predictability of robot motion , 2013, 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[20]  Maria Fox,et al.  Explainable Planning , 2017, ArXiv.

[21]  Anca D. Dragan,et al.  Robot Planning with Mathematical Models of Human State and Action , 2017, ArXiv.

[22]  Lars Karlsson,et al.  Grandpa Hates Robots - Interaction Constraints for Planning in Inhabited Environments , 2014, AAAI.

[23]  Yu Zhang,et al.  Behavior Explanation as Intention Signaling in Human-Robot Teaming , 2018, 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN).

[24]  Subbarao Kambhampati,et al.  (When) Can AI Bots Lie? , 2019, AIES.

[25]  Matthew Klenk,et al.  Resource Bounded Secure Goal Obfuscation , 2018 .

[26]  Miquel Ramírez,et al.  Action Selection for Transparent Planning , 2018, AAMAS.

[27]  Daniele Magazzeni,et al.  Towards Providing Explanations for AI Planner Decisions , 2018, IJCAI 2018.

[28]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[29]  Yu Zhang,et al.  Explicable Robot Planning as Minimizing Distance from Expected Behavior , 2016, ArXiv.

[30]  Michal Štolba Reveal or Hide: Information Sharing in Multi-Agent Planning , 2017 .

[31]  Yu Zhang,et al.  AI Challenges in Human-Robot Cognitive Teaming , 2017, ArXiv.

[32]  Yu Zhang,et al.  Plan explicability and predictability for robot task planning , 2015, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[33]  Yu Zhang,et al.  Progressive Explanation Generation for Human-robot Teaming , 2019, ArXiv.

[34]  Sebastian Sardiña,et al.  Cost-Based Goal Recognition for Path-Planning , 2017, AAMAS.

[35]  Meir Kalech,et al.  Goal and Plan Recognition Design for Plan Libraries , 2019, ACM Trans. Intell. Syst. Technol..

[36]  Yu Zhang,et al.  Explain by Goal Augmentation : Explanation Generation as Inverse Planning , 2010 .

[37]  Subbarao Kambhampati,et al.  Plan Explanations as Model Reconciliation - An Empirical Study , 2018, ArXiv.

[38]  Erez Karpas,et al.  Privacy Preserving Plans in Partially Observable Environments , 2016, IJCAI.

[39]  Siddhartha S. Srinivasa,et al.  Generating Legible Motion , 2013, Robotics: Science and Systems.

[40]  Maartje M. A. de Graaf,et al.  People's Explanations of Robot Behavior Subtly Reveal Mental State Inferences , 2019, 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[41]  S. Kambhampati,et al.  Plan Explicability for Robot Task Planning , 2010 .

[42]  Nancy M. Amato,et al.  A Roadmap for US Robotics - From Internet to Robotics 2020 Edition , 2021, Found. Trends Robotics.

[43]  Subbarao Kambhampati,et al.  A Unified Framework for Planning in Adversarial and Cooperative Environments , 2018, AAAI.

[44]  Anca D. Dragan,et al.  Expressing Robot Incapability , 2018, 2018 13th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[45]  David E. Smith Planning as an Iterative Process , 2012, AAAI.

[46]  Sailik Sengupta,et al.  RADAR - A Proactive Decision Support System for Human-in-the-Loop Planning , 2017, AAAI Fall Symposia.

[47]  Subbarao Kambhampati,et al.  Handling Model Uncertainty and Multiplicity in Explanations via Model Reconciliation , 2018, ICAPS.

[48]  Yu Zhang,et al.  Online Explanation Generation for Human-Robot Teaming , 2019, ArXiv.

[49]  Jason M. O'Kane,et al.  Finding plans subject to stipulations on what information they divulge , 2018, WAFR.

[50]  Subbarao Kambhampati,et al.  Hierarchical Expertise-Level Modeling for User Specific Robot-Behavior Explanations , 2020, AAAI.

[51]  Helen Owton,et al.  Sorry , 2018 .

[52]  Subbarao Kambhampati,et al.  Algorithms for the Greater Good! On Mental Modeling and Acceptable Symbiosis in Human-AI Collaboration , 2018, ArXiv.

[53]  Erez Karpas,et al.  Goal Recognition Design for Non-Optimal Agents , 2015, AAAI.