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1. A Logic Programming Approach to Regression Based Repair of Incorrect Initial Belief States

   Loc Pham, Enrico Pontelli (July 2020, International Conference on Logic Programming)

   null (Ed.)

   This paper introduces a combination of regression and belief revision to allow agents to deal with inconsistencies while executing plans. Starting from an inconsistent history consisting of actions and observations, the proposed framework (1) computes the initial belief states that support the actions and observations and (2) uses a belief revision operator to repair the false initial belief state. The framework operates on domains with static causal laws and supports arbitrary sequences of actions. The paper illustrates how logic programming can be effectively used to support these processes. 

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2. Automatic generation of interactive NPC scripts for a mixed-reality integrated learning environment

   Yuan, A.; Ke, F.; Naglieri, R.; Dai, Z.; Pachman, M. (October 2018, the International Conference Proceedings Series by ACM, 2018 10th International Conference on Education Technology and Computers (ICETC 2018) Proceedings Series by ACM (ISBN: 978-1-4503-6517-8))

   The Mixed-Reality Integrated Learning Environment (MILE) developed at Florida State University is a virtual reality based, inclusive and immersive e-learning environment that promotes engaging and effective learning interactions for a diversified learner population. MILE uses a large number of interactive Non-Player Characters (NPCs) to represent diverse research-based learner archetypes and groups, and to prompt and provide feedback for in situ teaching practice. The NPC scripts in MILE are written in Linden Scripting Language (LSL), and can be quite complex, creating a significant challenge in the development and maintenance of the system. To address this challenge, we develop NPC_GEN, an automatic NPC script generation tool that takes high-level NPC descriptions as input and automatically produces LSL scripts for NPCs. In this work, we introduce NPCDL, a language that we design for NPC_GEN to give high-level descriptions of NPCs, describe how NPC_GEN translates an NPCDL description into an LSL script, and report a user study of NPC_GEN. The results of our user study indicate that with minimal training, non-technical people are able to write and modify NPCDL descriptions, which can then be used to generate LSL scripts for the NPCs: the development and maintenance of NPCs is greatly simplified with NPC_GEN. 

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3. Integrated Task and Motion Planning for Safe Legged Navigation in Partially Observable Environments

   https://doi.org/10.1109/TRO.2023.3299524  

   Shamsah, Abdulaziz; Gu, Zhaoyuan; Warnke, Jonas; Hutchinson, Seth; Zhao, Ye (August 2023, IEEE Transactions on Robotics)

   This study proposes a hierarchically integrated framework for safe task and motion planning (TAMP) of bipedal locomotion in a partially observable environment with dynamic obstacles and uneven terrain. The high-level task planner employs linear temporal logic for a reactive game synthesis between the robot and its environment and provides a formal guarantee on navigation safety and task completion. To address environmental partial observability, a belief abstraction model is designed by partitioning the environment into multiple belief regions and employed at the high-level navigation planner to estimate the dynamic obstacles' location. This additional location information of dynamic obstacles offered by belief abstraction enables less conservative long-horizon navigation actions beyond guaranteeing immediate collision avoidance. Accordingly, a synthesized action planner sends a set of locomotion actions to the middle-level motion planner while incorporating safe locomotion specifications extracted from safety theorems based on a reduced-order model (ROM) of the locomotion process. The motion planner employs the ROM to design safety criteria and a sampling algorithm to generate nonperiodic motion plans that accurately track high-level actions. At the low level, a foot placement controller based on an angular-momentum linear inverted pendulum model is implemented and integrated with an ankle-actuated passivity-based controller for full-body trajectory tracking. To address external perturbations, this study also investigates the safe sequential composition of the keyframe locomotion state and achieves robust transitions against external perturbations through reachability analysis. The overall TAMP framework is validated with extensive simulations and hardware experiments on bipedal walking robots Cassie and Digit designed by Agility Robotics. 

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4. Pet dogs (Canis familiaris) re-engage humans after joint activity

   https://doi.org/10.1007/s10071-023-01774-1  

   Byrne, Molly; Horschler, Daniel J; Schmitt, Mark; Johnston, Angie M (July 2023, Animal Cognition)

   Abstract Joint intentionality, the mutual understanding of shared goals or actions to partake in a common task, is considered an essential building block of theory of mind in humans. Domesticated dogs are unusually adept at comprehending human social cues and cooperating with humans, making it possible that they possess behavioral signatures of joint intentionality in interactions with humans. Horschler and colleagues (Anim Behav 183: 159–168, 2022) examined joint intentionality in a service dog population, finding that upon interruption of a joint experience, dogs preferentially re-engaged their former partner over a passive bystander, a behavior argued to be a signature of joint intentionality in human children. In the current study, we aimed to replicate and extend these results in pet dogs. One familiar person played with the dog and then abruptly stopped. We examined if dogs would preferentially re-engage the player instead of a familiar bystander who was also present. Consistent with the findings of Horschler and colleagues (Anim Behav 183: 159–168, 2022), pet dogs preferentially gazed toward and offered the toy to the player significantly more than the familiar bystander. However, no difference was observed in physical contact. These findings provide preliminary evidence for behavioral signatures of joint intentionality in pet dogs, but future work is needed to understand whether this phenomenon extends to other contexts. 

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5. A Logic Programming Approach to Regression-Based Repair of Incorrect Initial Belief States

   https://doi.org/10.1007/978-3-030-67438-0_5  

   Tardivo, F. (January 2021, Lecture notes in computer science)

   This paper explores the challenge of encountering incorrect beliefs in the context of reasoning about actions and changes using action languages with sensing actions. An incorrect belief occurs when some observations conflict with the agent’s own beliefs. A common approach to recover from this situation is to replace the initial beliefs with beliefs that conform to the sequence of actions and the observations. The paper introduces a regression-based and revision-based approach to calculate a correct initial belief. Starting from an inconsistent history consisting of actions and observations, the proposed framework (1) computes the initial belief states that support the actions and observations and (2) uses a belief revision operator to repair the false initial belief state. The framework operates on domains with static causal laws, supports arbitrary sequences of actions, and integrates belief revision methods to select a meaningful initial belief state among possible alternatives. 

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