Adaptive planning in human search

How do people plan ahead when searching for rewards? We investigate planning in a foraging task in which participants search for rewards on an infinite two-dimensional grid. Our results show that their search is best-described by a model which searches at least 3 steps ahead. Furthermore, participants do not seem to update their beliefs during planning, but rather treat their initial beliefs as given, a strategy similar to a heuristic called root-sampling. This planning algorithm corresponds well with participants’ behavior in test problems with restricted movement and varying degrees of information, outperforming more complex models. These results enrich our understanding of adaptive planning in complex environments

Autonomous Search and Rescue with Modeling and Simulation and Metrics

Unmanned Aerial Vehicles (UAVs) provide rapid exploration capabilities in search and rescue missions while accepting more risks than human operations. One limitation in that current UAVs are heavily manpower intensive and such manpower demands limit abilities to expand UAV use. In operation, manpower demands in UAVs range from determining tasks, selecting waypoints, manually controlling platforms and sensors, and tasks in between. Often, even a high level of autonomy is possible with human generated objectives and then autonomous resource allocation, routing, and planning. However, manually generating tasks and scenarios is still manpower intensive. To reduce manpower demands and move towards more autonomous operations, the authors develop an adaptive planning system that takes high level goals from a human operator and translates them into situationally relevant tasking. For expository simulation, the authors further describe constructing a scenario around the 2018 Hawaii Puna lava natural disaster

Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search

Target search with unmanned aerial vehicles (UAVs) is relevant problem to many scenarios, e.g., search and rescue (SaR). However, a key challenge is planning paths for maximal search efficiency given flight time constraints. To address this, we propose the Obstacle-aware Adaptive Informative Path Planning (OA-IPP) algorithm for target search in cluttered environments using UAVs. Our approach leverages a layered planning strategy using a Gaussian Process (GP)-based model of target occupancy to generate informative paths in continuous 3D space. Within this framework, we introduce an adaptive replanning scheme which allows us to trade off between information gain, field coverage, sensor performance, and collision avoidance for efficient target detection. Extensive simulations show that our OA-IPP method performs better than state-of-the-art planners, and we demonstrate its application in a realistic urban SaR scenario.Comment: Paper accepted for International Conference on Robotics and Automation (ICRA-2019) to be held at Montreal, Canad

Conflict resolution by random estimated costs

Conflict resolution is an important part of many intelligent systems such as production systems, planning tools and cognitive architectures. For example, the ACT-R cognitive architecture (Anderson and Lebiere, 1998) uses a powerful conflict resolution theory that allowed for modelling many characteristics of human decision making. The results of more recent works, however, pointed to the need of revisiting the conflict resolution theory of ACT-R to incorporate more dynamics. In the proposed theory the conflict is resolved using the estimates of the expected costs of production rules. The method has been implemented as a stand alone search program as well as an add-on to the ACT-R architecture replacing the standard mechanism. The method expresses more dynamic and adaptive behaviour. The performance of the algorithm shows that it can be successfully used as a search and optimisation technique

Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework

In this paper, we argue that the future of Artificial Intelligence research resides in two keywords: integration and embodiment. We support this claim by analyzing the recent advances of the field. Regarding integration, we note that the most impactful recent contributions have been made possible through the integration of recent Machine Learning methods (based in particular on Deep Learning and Recurrent Neural Networks) with more traditional ones (e.g. Monte-Carlo tree search, goal babbling exploration or addressable memory systems). Regarding embodiment, we note that the traditional benchmark tasks (e.g. visual classification or board games) are becoming obsolete as state-of-the-art learning algorithms approach or even surpass human performance in most of them, having recently encouraged the development of first-person 3D game platforms embedding realistic physics. Building upon this analysis, we first propose an embodied cognitive architecture integrating heterogenous sub-fields of Artificial Intelligence into a unified framework. We demonstrate the utility of our approach by showing how major contributions of the field can be expressed within the proposed framework. We then claim that benchmarking environments need to reproduce ecologically-valid conditions for bootstrapping the acquisition of increasingly complex cognitive skills through the concept of a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017 conference (Lisbon, Portugal

Online, interactive user guidance for high-dimensional, constrained motion planning

We consider the problem of planning a collision-free path for a high-dimensional robot. Specifically, we suggest a planning framework where a motion-planning algorithm can obtain guidance from a user. In contrast to existing approaches that try to speed up planning by incorporating experiences or demonstrations ahead of planning, we suggest to seek user guidance only when the planner identifies that it ceases to make significant progress towards the goal. Guidance is provided in the form of an intermediate configuration $\hat{q}$, which is used to bias the planner to go through $\hat{q}$. We demonstrate our approach for the case where the planning algorithm is Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our approach allows to compute highly-constrained paths with little domain knowledge. Without our approach, solving such problems requires carefully-crafting domain-dependent heuristics

Online, interactive user guidance for high-dimensional, constrained motion planning

We consider the problem of planning a collision-free path for a high-dimensional robot. Specifically, we suggest a planning framework where a motion-planning algorithm can obtain guidance from a user. In contrast to existing approaches that try to speed up planning by incorporating experiences or demonstrations ahead of planning, we suggest to seek user guidance only when the planner identifies that it ceases to make significant progress towards the goal. Guidance is provided in the form of an intermediate configuration $\hat{q}$, which is used to bias the planner to go through $\hat{q}$. We demonstrate our approach for the case where the planning algorithm is Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our approach allows to compute highly-constrained paths with little domain knowledge. Without our approach, solving such problems requires carefully-crafting domain-dependent heuristics