In this paper, we introduce an approach to validate the functional equivalence of planning domain models. Validating the functional equivalence of planning domain models is the problem of formally confirming that two planning domain models can be used to solve the same set of problems. The need for techniques to validate the functional equivalence of planning domain models has been highlighted in previous research and has applications in model learning, development and extension. We prove the soundness and completeness of our method. We also develop D-VAL, an automatic functional equivalence validation tool for planning domain models. Empirical evaluation shows that D-VAL validates the functional equivalence of most examined domains in less than five minutes. Additionally, we provide a benchmark to evaluate the feasibility and scalability of this and future related work.

Intelligent agents powered by AI planning assist people in complex scenarios, such as managing teams of semi-autonomous vehicles. However, AI planning models may be incomplete, leading to plans that do not adequately meet the stated objectives, especially in unpredicted situations. Humans, who are apt at identifying and adapting to unusual situations, may be able to assist planning agents in these situations by encoding their knowledge into a planner at run-time. We investigate whether people can collaborate with agents by providing their knowledge to an agent using linear temporal logic (LTL) at run-time without changing the agent's domain model. We presented 24 participants with baseline plans for situations in which a planner had limitations, and asked the participants for workarounds for these limitations. We encoded these workarounds as LTL constraints. Results show that participants' constraints improved the expected return of the plans by 10% ($p < 0.05$) relative to baseline plans, demonstrating that human insight can be used in collaborative planning for resilience. However, participants used more declarative than control constraints over time, but declarative constraints produced plans less similar to the expectation of the participants, which could lead to potential trust issues.

Partially Observable Monte-Carlo Planning (POMCP) is a powerful online algorithm able to generate approximate policies for large Partially Observable Markov Decision Processes. The online nature of this method supports scalability by avoiding complete policy representation. The lack of an explicit representation however hinders policy interpretability and makes policy verification very complex. In this work, we propose two contributions. The first is a method for identifying unexpected actions selected by POMCP with respect to expert prior knowledge of the task. The second is a shielding approach that prevents POMCP from selecting unexpected actions. The first method is based on Satisfiability Modulo Theory (SMT). It inspects traces (i.e., sequences of belief-action-observation triplets) generated by POMCP to compute the parameters of logical formulas about policy properties defined by the expert. The second contribution is a module that uses online the logical formulas to identify anomalous actions selected by POMCP and substitutes those actions with actions that satisfy the logical formulas fulfilling expert knowledge. We evaluate our approach on Tiger, a standard benchmark for POMDPs, and a real-world problem related to velocity regulation in mobile robot navigation. Results show that the shielded POMCP outperforms the standard POMCP in a case study in which a wrong parameter of POMCP makes it select wrong actions from time to time. Moreover, we show that the approach keeps good performance also if the parameters of the logical formula are optimized using trajectories containing some wrong actions.

The domain of Embodied AI has recently witnessed substantial progress, particularly in navigating agents within their environments. These early successes have laid the building blocks for the community to tackle tasks that require agents to actively interact with objects in their environment. Object manipulation is an established research domain within the robotics community and poses several challenges including manipulator motion, grasping and long-horizon planning, particularly when dealing with oft-overlooked practical setups involving visually rich and complex scenes, manipulation using mobile agents (as opposed to tabletop manipulation), and generalization to unseen environments and objects. We propose a framework for object manipulation built upon the physics-enabled, visually rich AI2-THOR framework and present a new challenge to the Embodied AI community known as ArmPointNav. This task extends the popular point navigation task to object manipulation and offers new challenges including 3D obstacle avoidance, manipulating objects in the presence of occlusion, and multi-object manipulation that necessitates long term planning. Popular learning paradigms that are successful on PointNav challenges show promise, but leave a large room for improvement.

This paper presents a novel algorithm, called MRRT, which uses multiple rapidly-exploring random trees for fast online replanning of autonomous vehicles in dynamic environments with moving obstacles. The proposed algorithm is built upon the RRT algorithm with a multi-tree structure. At the beginning, the RRT algorithm is applied to find the initial solution based on partial knowledge of the environment. Then, the robot starts to execute this path. At each iteration, the new obstacle configurations are collected by the robot's sensor and used to replan the path. This new information can come from unknown static obstacles (e.g., seafloor layout) as well as moving obstacles. Then, to accommodate the environmental changes, two procedures are adopted: 1) edge pruning, and 2) tree regrowing. Specifically, the edge pruning procedure checks the collision status through the tree and only removes the invalid edges while maintaining the tree structure of already-explored regions. Due to removal of invalid edges, the tree could be broken into multiple disjoint trees. As such, the RRT algorithm is applied to regrow the trees. Specifically, a sample is created randomly and joined to all the disjoint trees in its local neighborhood by connecting to the nearest nodes. Finally, a new solution is found for the robot. The advantages of the proposed MRRT algorithm are as follows: i) retains the maximal tree structure by only pruning the edges which collide with the obstacles, ii) guarantees probabilistic completeness, and iii) is computational efficient for fast replanning since all disjoint trees are maintained for future connections and expanded simultaneously.

Registered reports have been proposed as a way to move from eye-catching and surprising results and toward methodologically sound practices and interesting research questions. However, none of the top-twenty artificial intelligence journals support registered reports, and no traces of registered reports can be found in the field of artificial intelligence. Is this because they do not provide value for the type of research that is conducted in the field of artificial intelligence? Registered reports have been touted as one of the solutions to the problems surrounding the reproducibility crisis. They promote good research practices and combat data dredging1.

Automated adversary emulation is becoming an indispensable tool of network security operators in testing and evaluating their cyber defenses. At the same time, it has exposed how quickly adversaries can propagate through the network. While research has greatly progressed on quality decoy generation to fool human adversaries, we may need different strategies to slow computer agents. In this paper, we show that decoy generation can slow an automated agent's decision process, but that the degree to which it is inhibited is greatly dependent on the types of objects used. This points to the need to explicitly evaluate decoy generation and placement strategies against fast moving, automated adversaries.

This paper addresses the path planning problems for recommending parking spaces, given the difficulties of identifying the most optimal route to vacant parking spaces and the shortest time to leave the parking space. Our optimization approach is based on the entropy method and realized by the A* algorithm. Experiments have shown that the combination of A* and the entropy value induces the optimal parking solution with the shortest route while being robust to environmental factors.

Task environments developed in Minecraft are becoming increasingly popular for artificial intelligence (AI) research. However, most of these are currently constructed manually, thus failing to take advantage of procedural content generation (PCG), a capability unique to virtual task environments. In this paper, we present mcg, an open-source library to facilitate implementing PCG algorithms for voxel-based environments such as Minecraft. The library is designed with human-machine teaming research in mind, and thus takes a 'top-down' approach to generation, simultaneously generating low and high level machine-readable representations that are suitable for empirical research. These can be consumed by downstream AI applications that consider human spatial cognition. The benefits of this approach include rapid, scalable, and efficient development of virtual environments, the ability to control the statistics of the environment at a semantic level, and the ability to generate novel environments in response to player actions in real time.

Parsons Corporation (NYSE: PSN) is developing and deploying artificial intelligence (AI) across a wide array of federal solutions and critical infrastructure projects to solve our customer's most challenging problems, produce actionable intelligence, and improve the user experience. Data analytics, AI, and edge computing are ingrained in company offerings across all business units. For example, the company developed an AI-enabled weapon-target pairing algorithm, with initial tests showing outstanding accuracy and speed results; produced an electronic warfare (EW) planning optimization tool-set named TEMPO (Tactical Electronic Warfare Machine Learning Planning Optimization); and recently won a classified research and development contract to develop constellation task scheduling algorithms based on organically developed AI technology. "Parsons' artificial intelligence capabilities align with our customer's vision by improving situational awareness, decision-making, the safety of operating equipment, streamlining business processes, and protecting critical infrastructure," said Ricardo Lorenzo, chief technology officer for Parsons. "By combining our AI technical expertise with our operational understanding of the all-domain environment and critical infrastructure markets, we're working closely with our customers to develop leap-ahead technology that empowers operators at the tactical edge and beyond. We're also developing differentiated capabilities that ensure the efficiency and security of existing energy and water networks."