bluuebunny/crossref_metadata_2025 huggingface dataset:
Clothoidal Local Path Template for Intention Estimation by Assistive Mobile Robots
Authors: Denis Kevin , Philips Johan , Bruyninckx Herman , Demeester Eric
Published: September 2022

This work proposes and evaluates various improvements to a circular local path template (LPT) that we have designed in the past to estimate driver intents and to provide navigation assistance to wheelchair drivers. This LPT may also be useful in other mobile robotics applications such as for intuitive teleoperated control, for fast collision checking in path planning or for obstacle avoidance algorithms. The LPT consists of a large but fixed set of paths in the mobile robot’s local neighborhood. Based on an efficient look-up table, the LPT paths’ lengths are adjusted such that they are collisionfree. However, experiments have shown that in dense environments insufficient circular paths are found, which impedes correct intention estimation and thus navigation assistance. In this work, the use of clothoidal paths rather than circular paths is evaluated. This substantially improves the capability to find complex paths in dense areas. Furthermore, we adapt the LPT to deal with dynamic obstacles of random shape using motion prediction estimates of these objects.
Open-World Reasoning for Service Robots
Authors: Jiang Yuqian , Walker Nick , Hart Justin , Stone Peter
Published: September 2022

A service robot accepting verbal commands from a human operator is likely to encounter requests that reference objects not currently represented in its knowledge base. In domestic or office settings, the construction of a complete knowledge base would be cumbersome and unlikely to succeed in most real-world deployments. The world that such a robot operates in is thus “open” in the sense that some objects that it must act on in the real world are not described in its internal representation. However, when an operator gives a command referencing an object that the robot has not yet observed (and thus not incorporated into its knowledge base), we can think of the object as being hypothetical to the robot. This paper presents a novel method for closing the robot’s world model for planning purposes by introducing hypothetical objects into the robot’s knowledge base, reasoning about these hypothetical objects, and acting on these hypotheses in the real world. We use our implementation of this method on a domestic service robot as an illustrative demonstration to explore how it works in practice.
Generalized Lazy Search for Robot Motion Planning:Interleaving Search and Edge Evaluation via Event-Based Toggles
Authors: Mandalika Aditya , Choudhury Sanjiban , Salzman Oren , Srinivasa Siddhartha
Published: September 2022

Lazy search algorithms can efficiently solve problems where edge evaluation is the bottleneck in computation, as is the case for robotic motion planning. The optimal algorithm in this class, LazySP, lazily restricts edge evaluation to only the shortest path. Doing so comes at the expense of search effort, i.e., LazySP must recompute the search tree every time an edge is found to be invalid. This becomes prohibitively expensive when dealing with large graphs or highly cluttered environments. Our key insight is the need to balance both edge evaluation and search effort to minimize the total planning time. Our contribution is two-fold. First, we propose a framework, Generalized Lazy Search (GLS), that seamlessly toggles between search and evaluation to prevent wasted efforts. We show that for a choice of toggle, GLS is provably more efficient than LazySP. Second, we leverage prior experience of edge probabilities to derive GLS policies that minimize expected planning time. We show that GLS equipped with such priors significantly outperforms competitive baselines for many simulated environments in R2,SE(2) and 7-DoF manipulation.
Learning Heuristic Functions for Mobile Robot Path Planning Using Deep Neural Networks
Authors: Takahashi Takeshi , Sun He , Tian Dong , Wang Yebin
Published: September 2022

Resorting to certain heuristic functions to guide the search, the computational efficiency of prevailing path planning algorithms, such as A*, D*, and their variants, is solely determined by how well the heuristic function approximates the true path cost. In this study, we propose a novel approach to learning heuristic functions using a deep neural network (DNN) to improve the computational efficiency. Even though DNNs have been widely used for object segmentation, natural language processing, and perception, their role in helping to solve path planning problems has not been well investigated. This work shows how DNNs can be applied to path planning and what kind of loss functions are suitable for learning such a heuristic. Our preliminary results show that an appropriately designed and trained DNN can learn a heuristic that effectively guides prevailing path planning algorithms.
A Hierarchical Approach to Active Semantic Mapping Using Probabilistic Logic and Information Reward POMDPs
Authors: Veiga Tiago S. , Silva Miguel , Ventura Rodrigo , Lima Pedro U.
Published: September 2022

Maintaining a semantic map of a complex and dynamic environment, where the uncertainty originates in both noisy perception and unexpected changes, is a challenging problem. In particular, we focus on the problem of maintaining a semantic map of an environment by a mobile agent. In this paper we address this problem in an hierarchical fashion. Firstly, we employ a probabilistic logic model representing the semantic map, as well as the associated uncertainty. Secondly, we model the interaction of the robot with the environment with a set of information-reward POMDP models, one for each partition of the environment (e.g., a room). The partition is performed in order to address the scalability limitations of POMDP models over very large state spaces. We then use probabilistic inference to determine which POMDP and policy to execute next. Experimental results show the efficiency of this architecture in real domestic service robotic scenarios.
A Logical Semantics for PDDL+
Authors: Batusov Vitaliy , Soutchanski Mikhail
Published: September 2022

PDDL+ is an extension of PDDL2.1 which incorporates fully-featured autonomous processes and allows for better modelling of mixed discrete-continuous domains. Unlike PDDL2.1, PDDL+ lacks a logical semantics, relying instead on state-transitional semantics enriched with hybrid automata semantics for the continuous states. This complex semantics makes analysis and comparisons to other action formalisms difficult. In this paper, we propose a natural extension of Reiter’s situation calculus theories inspired by hybrid automata. The kinship between PDDL+ and hybrid automata allows us to develop a direct mapping between PDDL+ and situation calculus, thereby supplying PDDL+ with a logical semantics and the situation calculus with a modern way of representing autonomous processes. We outline the potential benefits of the mapping by suggesting a new approach to effective planning in PDDL+.
A Factored Approach to Deterministic Contingent Multi-Agent Planning
Authors: Shekhar Shashank , Brafman Ronen I. , Shani Guy
Published: September 2022

Collaborative Multi-Agent Planning (MAP) under uncertainty with partial observability is a notoriously difficult problem. Such MAP problems are often modeled as DecPOMDPs, or its qualitative variant, QDec-POMDP, which is essentially a MAP version of contingent planning. The QDecPOMDP model was introduced with the hope that its simpler, non-probabilistic structure will allow for better scalability. Indeed, at least with deterministic actions, the recent IMAP algorithm scales much better than comparable DecPOMDP algorithms (Bazinin and Shani 2018). In this work we suggest a new approach to solving Deterministic QDecPOMDPs based on problem factoring. First, we find a solution to a MAP problem where the results of any observation is available to all agents. This is essentially a single-agent planning problem for the entire team. Then, we project the solution tree into sub-trees, one per agent, and let each agent transform its projected tree into a legal local tree. If all agents succeed, we combine the trees into a valid joint-plan. Otherwise, we continue to explore the space of team solutions. This approach is sound, complete, and as our empirical evaluation demonstrates, scales much better than the IMAP algorithm.
Optimizing Parameters for Uncertain Execution and Rescheduling Robustness
Authors: Chi Wayne , Agrawal Jagriti , Chien Steve , Fosse Elyse , Guduri Usha
Published: September 2022

We describe use of Monte Carlo simulation to optimize schedule parameters for execution and rescheduling robustness in the face of execution uncertainties. We search in the activity input parameter space where a) the onboard scheduler is a one shot non-backtracking scheduler and b) the activity input priority determines the order in which activities are considered for placement in the schedule. We show that simulation driven search for activity parameters outperforms static priority assignment. Our approach can be viewed as using simulation feedback to determine problem specific heuristics e.g. Squeaky Wheel Optimization. These techniques are currently baselined for use in the ground operations of NASA’s next planetary rover, the Mars 2020 rover.
Deep Policies for Width-Based Planning in Pixel Domains
Authors: Junyent Miquel , Jonsson Anders , Gómez Vicenç
Published: September 2022

Width-based planning has demonstrated great success in recent years due to its ability to scale independently of the size of the state space. For example, Bandres et al. (2018) introduced a rollout version of the Iterated Width algorithm whose performance compares well with humans and learning methods in the pixel setting of the Atari games suite. In this setting, planning is done on-line using the “screen” states and selecting actions by looking ahead into the future. However, this algorithm is purely exploratory and does not leverage past reward information. Furthermore, it requires the state to be factored into features that need to be pre-defined for the particular task, e.g., the B-PROST pixel features. In this work, we extend width-based planning by incorporating an explicit policy in the action selection mechanism. Our method, called π-IW, interleaves width-based planning and policy learning using the state-actions visited by the planner. The policy estimate takes the form of a neural network and is in turn used to guide the planning step, thus reinforcing promising paths. Surprisingly, we observe that the representation learned by the neural network can be used as a feature space for the width-based planner without degrading its performance, thus removing the requirement of pre-defined features for the planner. We compare π-IW with previous widthbased methods and with AlphaZero, a method that also interleaves planning and learning, in simple environments, and show that π-IW has superior performance. We also show that π-IW algorithm outperforms previous width-based methods in the pixel setting of Atari games suite.
Mars On-Site Shared Analytics Information and Computing
Authors: Hook Joshua Vander , Vaquero Tiago , Rossi Federico , Troesch Martina , Net Marc Sanchez , Schoolcraft Joshua , Croix Jean-Pierre de la , Chien Steve
Published: September 2022

We study the use of distributed computation in a representative multi-robot planetary exploration mission. We model a network of small rovers with access to computing resources from a static base station based on current design efforts and extrapolation from the Mars 2020 rover autonomy. The key algorithmic problem is simultaneous scheduling of computation, communication, and caching of data, as informed by an autonomous mission planner. We consider scheduling of a dependency chain of required and optional (but rewarding) tasks and present a consensus-backed scheduler for sharedworld, distributed scheduling based on an Integer Linear Program. We validate the pipeline with simulation and field results. Our results are intended to provide a baseline comparison and motivating application domain for future research into network-aware decentralized scheduling and resource allocation.
Probabilistic Robust Multi-Agent Path Finding
Authors: Atzmon Dor , Stern Roni , Felner Ariel , Sturtevant Nathan R. , Koenig Sven
Published: September 2022

In a multi-agent path finding (MAPF) problem, the task is to move a set of agents to their goal locations without conflicts. In the real world, unexpected events may delay some of the agents. In this paper, we therefore study the problem of finding a p-robust solution to a given MAPF problem, which is a solution that succeeds with probability at least p, even though unexpected delays may occur. We propose two methods for verifying that given solutions are p-robust. We also introduce an optimal CBS-based algorithm, called pR-CBS, and a fast suboptimal algorithm, called pR-GCBS, for finding such solutions. Our experiments show that a p-robust solution reduces the number of conflicts compared to optimal, non-robust solutions.
A Novel Lookahead Strategy for Delete Relaxation Heuristics in Greedy Best-First Search
Authors: Fickert Maximilian
Published: September 2022

Best-first width search (BFWS) is a recent approach to satisficing planning that combines traditional heuristics with novelty measures to achieve a balance between exploration and effective search guidance (exploitation). One such novelty measure is based on counting the number of subgoals achieved on the path from a state in which a relaxed plan was computed. We introduce a new lookahead strategy for greedy best-first search based on this idea, where after each expansion, a bounded lookahead search is guided by relaxed subgoal counting. Furthermore, we combine this technique with partial delete relaxation heuristics to improve the subgoals. Using the hCFF heuristic with online-refinement of conjunctions, we obtain a planner that significantly outperforms the state of the art in satisficing planning on the IPC benchmarks.
Strengthening Potential Heuristics with Mutexes and Disambiguations
Authors: Fišer Daniel , Horčík Rostislav , Komenda Antonín
Published: September 2022

Potential heuristics assign a numerical value (potential) to each fact and compute the heuristic value for a given state as the sum of these potentials. A mutex is an invariant stating that a certain combination of facts cannot be part of any reachable state. In this paper, we use mutexes to improve potential heuristics in two ways. First, we show that the mutex-based disambiguations of the goal and preconditions of operators leads to a less constrained linear program yielding stronger heuristics. Second, we utilize mutexes in a construction of new optimization functions based on counting of the number of states containing certain sets of facts. The experimental evaluation shows a significant increase in the number of solved tasks.
Analyzing and Avoiding Pathological Behavior in Parallel Best-First Search
Authors: Kuroiwa Ryo , Fukunaga Alex
Published: September 2022

Recent work has experimentally shown that parallelization of Greedy Best-First Search (GBFS), a satisficing best-first search method, can behave very differently from sequential GBFS. In this paper, we propose a theoretical framework to compare parallel best-first search with sequential best-first search, including both suboptimal (GBFS, Weighted A*) and optimal (A*) best-first search methods. We analyze the extent to which the search behavior of existing parallel best-first search methods differ from sequential best-first search, and show that existing methods are vulnerable to pathological behavior, and that they can expand nodes which would not be expanded by sequential search under any tie-breaking policy. We also propose PUHF, a parallel best-first search which is guaranteed to expand a node only if there is some tie-breaking strategy for sequential search which expands the node.
POMDP + Information-Decay: Incorporating Defender's Behaviour in Autonomous Penetration Testing
Authors: Schwartz Jonathon , Kurniawati Hanna , El-Mahassni Edwin
Published: September 2022

Penetration testing (pen-testing) aims to assess vulnerabilities in a computer network by emulating possible attacks. Autonomous pen-testing allows frequent and regular pen-testing to be performed, which is increasingly necessary as networks become larger and more complex. Autonomous pen-testing is a planning under uncertainty problem, where the uncertainty is caused by partial observability of the network, lack of reliability of attack tools, and possible changes in the network that are triggered by the network administrator (the defender). Approaches that account for the first two causes of uncertainty have been developed based on the mathematically principled framework, Partially Observable Markov Decision Process (POMDP). However, they do not account for the third type of uncertainty. On the other hand, work that accounts for the defender's actions do not account for both partial observability and unreliability of the attack tools. This paper proposes a POMDP-based autonomous pen-testing framework that accounts for the defender's behaviour, thereby accounting for all of the above three causes of uncertainty. Key to our model is the observation that the defender's actions can be abstracted into two types: Network analysis, which does not alter the network, and active defence operations, which alter the network. This observation enables us to represent the defender's behaviour as a single variable: An information decay factor. This variable is based on the expected time the defender takes to move from analysing to actively defending the network, and therefore represents the decay of a pen-tester's knowledge about the network. We propose D-PenTesting, which assumes the decay factor is known prior to execution, and LD-PenTesting, which learns the decay factor as it attempts to break into the network. Simulation tests on two benchmark scenarios indicate that D-PenTesting and LD-PenTesting outperform existing POMDP-based pen-tester and is more robust than one that incorporates a POMDP-based defender.
Contention-Aware Mapping and Scheduling Optimization for NoC-Based MPSoCs
Authors: Yan Rongjie , Zhou Yupeng , Cai Anyu , Li Changwen , Yan Yige , Yin Minghao
Published: September 2022

Network-on-Chip (NoC) has emerged as an alternative interconnecting paradigm in the state-of-the-art multi-core architectures. Its capability of parallel data transfer over various paths increases the possibility of resource contention and causes network congestion. How to avoid contention, as well as to optimize performance and energy consumption becomes a great challenge for system designers. In this paper, we consider spatial and temporal aspects of communication to avoid contention. In spatial aspect, we propose to utilize overlapped communication paths to estimate the degree of contention, such that they can be minimized in mapping stage. In temporal aspect, we sequentialize data transfer with potential contention by introducing additional latency. To optimize the design concerns with the corresponding constraint model, we further provide an efficient algorithm to search for better solutions for the problem, which integrates a local search process into a genetic algorithm and applies various heuristics in initialization and evolution process. Experimentations from random and real-case benchmarks demonstrate the efficiency of our method in multi-objective optimization and the effectiveness of our techniques in avoiding network contention, while keeping performance and energy consumption optimized.
Generating Explanations for Temporal Logic Planner Decisions
Authors: Kasenberg Daniel , Thielstrom Ravenna , Scheutz Matthias
Published: September 2022

Although temporal logic has been touted as a fruitful language for specifying interpretable agent objectives, there has been little emphasis on generating explanations for agents with temporal logic objectives. In this paper, we develop an approach to generating explanations for the behavior of agents planning with several temporal logic objectives. We focus on agents operating in deterministic Markov decision processes (MDPs), and specify objectives using linear temporal logic (LTL). Given an agent planning to maximally satisfy some set of LTL objectives (with an associated preference structure) in a deterministic MDP, we introduce an algorithm for constructing explanations answering both factual and “why” queries, which queries are also specified in LTL.
Learning Sequences of Approximations for Hierarchical Motion Planning
Authors: Brandão Martim , Havoutis Ioannis
Published: September 2022

The process of designing hierarchical motion planners typically involves problem-specific intuition and implementations. This process is sub-optimal both in terms of solution space (amount of possibilities for search-space approximations, choice of planner parameters, etc) and amount of human labour. In this paper we show that the design of hierarchical motion planners does not have to be manual. We present a method for parameterizing and then optimizing sequences of problem approximations used in hierarchical motion planning. We define these as a specific kind of graph with intermediate state-spaces and solutions as nodes, and costs and planner parameters as edge properties. These properties become a continuous optimization variable that changes the sequence and parameters of sub-planners in the hierarchy. Using Pareto-front estimation, our method automatically discovers multiple designs of optimal computation-time/motion-cost trade-offs. We evaluate the method on a set of legged robot motion planning problems where hand-designed hierarchies are abundant. Our method discovers sequences of problem approximations which achieve similar—though slightly higher—performance than the best human-designed hierarchies. The performance gain significantly increases on new problems, yielding 12x faster computation times and 10% higher success rates.
Imitation Learning over Heterogeneous Agents with Restraining Bolts
Authors: De Giacomo Giuseppe , Favorito Marco , Iocchi Luca , Patrizi Fabio
Published: September 2022

A common problem in Reinforcement Learning (RL) is that the reward function is hard to express. This can be overcome by resorting to Inverse Reinforcement Learning (IRL), which consists in first obtaining a reward function from a set of execution traces generated by an expert agent, and then making the learning agent learn the expert's behavior –this is known as Imitation Learning (IL). Typical IRL solutions rely on a numerical representation of the reward function, which raises problems related to the adopted optimization procedures.We describe an IL method where the execution traces generated by the expert agent, possibly via planning, are used to produce a logical (as opposed to numerical) specification of the reward function, to be incorporated into a device known as Restraining Bolt (RB). The RB can be attached to the learning agent to drive the learning process and ultimately make it imitate the expert. We show that IL can be applied to heterogeneous agents, with the expert, the learner and the RB using different representations of the environment's actions and states, without specifying mappings among their representations.
Reinforcement Learning for Zone Based Multiagent Pathfinding under Uncertainty
Authors: Ling Jiajing , Gupta Tarun , Kumar Akshat
Published: September 2022

We address the problem of multiple agents finding their paths from respective sources to destination nodes in a graph (also called MAPF). Most existing approaches assume that all agents move at fixed speed, and that a single node accommodates only a single agent. Motivated by the emerging applications of autonomous vehicles such as drone traffic management, we present zone-based path finding (or ZBPF) where agents move among zones, and agents' movements require uncertain travel time. Furthermore, each zone can accommodate multiple agents (as per its capacity). We also develop a simulator for ZBPF which provides a clean interface from the simulation environment to learning algorithms. We develop a novel formulation of the ZBPF problem using difference-of-convex functions (DC) programming. The resulting approach can be used for policy learning using samples from the simulator. We also present a multiagent credit assignment scheme that helps our learning approach converge faster. Empirical results in a number of 2D and 3D instances show that our approach can effectively minimize congestion in zones, while ensuring agents reach their final destinations.
The Consistent Case in Bidirectional Search and a Bucket-to-Bucket Algorithm as a Middle Ground between Front-to-End and Front-to-Front
Authors: Alcázar Vidal
Published: September 2022

Recently, the proposal of individual bounds that use consistent heuristics in front-to-end bidirectional search has improved the state of the art. However, modern theory in bidirectional search does not include algorithms that explicitly exploit consistency. Here we extend past theoretical work, namely must-expand pairs and derived concepts, to the case in which consistency is used, and clarify their relationship with the aforementioned individual bounds.

Departing from the new theory, we show that consistent front-to-end heuristics can also be seen as an admissible estimation of the lowest cost of any path between any two nodes. Therefore, by grouping nodes by g and their heuristic values in buckets, such an estimate can be computed for sets of nodes and not individual pairs without loss of information. This bucket-to-bucket computation, although as expensive as front-to-front in the worst case, is the state of the art in the Pancake Problem, and allows implementing near-optimal algorithms that exploit consistency. Also, experiments offer an insightful measurement of how far front-to-end algorithms are from their theoretical limit.
Explaining Path Plan Optimality: Fast Explanation Methods for Navigation Meshes Using Full and Incremental Inverse Optimization
Authors: Brandão Martim , Coles Amanda , Magazzeni Daniele
Published: September 2022

Path planners are important components of various products from video games to robotics, but their output can be counter-intuitive due to problem complexity. As a step towards improving the understanding of path plans by various users, here we propose methods that generate explanations for the optimality of paths. Given the question "why is path A optimal, rather than B which I expected?", our methods generate an explanation based on the changes to the graph that make B the optimal path. We focus on the case of path planning on navigation meshes, which are heavily used in the computer game industry and robotics. We propose two methods - one based on a single inverse-shortest-paths optimization problem, the other incrementally solving complex optimization problems. We show that these methods offer computation time improvements of up to 3 orders of magnitude relative to domain-independent search-based methods, as well as scaling better with the length of explanations. Finally, we show through a user study that, when compared to baseline cost-based explanations, our explanations are more satisfactory and effective at increasing users' understanding of problems.
Loop Detection in the PANDA Planning System
Authors: Höller Daniel , Behnke Gregor
Published: September 2022

The International Planning Competition (IPC) in 2020 was the first one for a long time to host tracks on Hierarchical Task Network (HTN) planning. HyperTensioN, the winner of the tack on totally-ordered problems, comes with an interesting technique: it stores parts of the decomposition path in the state to mark expanded tasks and forces its depth first search to leave recursive structures in the hierarchy. This can be seen as a form of loop detection (LD) – a technique that is not very common in HTN planning. This might be due to the spirit of encoding enough advice in the model to find plans (so that loop detection is simply not necessary), or because it becomes a computationally hard task in the general (i.e. partially-ordered) setting. We integrated several approximate and exact techniques for LD into the progression search of the HTN planner PANDA. We test our techniques on the benchmark set of the IPC 2020. Both in the partial ordered and total ordered track, PANDA with LD performs better than the respective winner of the competition.
Rule-based Shielding for Partially Observable Monte-Carlo Planning
Authors: Mazzi Giulio , Castellini Alberto , Farinelli Alessandro
Published: September 2022

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 substitute 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 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.
GRAND-VISION: An Intelligent System for Optimized Deployment Scheduling of Law Enforcement Agents
Authors: Chase Jonathan , Phong Tran , Long Kang , Le Tony , Lau Hoong Chuin
Published: September 2022

Law enforcement agencies in dense urban environments, faced with a wide range of incidents to handle and limited manpower, are turning to data-driven AI to inform their policing strategy. In this paper we present a patrol scheduling system called GRAND-VISION: Ground Response Allocation and Deployment - Visualization, Simulation, and Optimization. The system employs deep learning to generate incident sets that are used to train a patrol schedule that can accommodate varying manpower, break times, manual pre-allocations, and a variety of spatio-temporal demand features. The complexity of the scenario results in a system with real world applicability, which we demonstrate through simulation on historical data obtained from a large urban law enforcement agency.
vPlanSim: An Open Source Graphical Interface for the Visualisation and Simulation of AI Systems
Authors: Roberts Jamie O. , Mastorakis Georgios , Lazaruk Brad , Franco Santiago , Stokes Adam A. , Bernardini Sara
Published: September 2022

We introduce vPlanSim, an open source tool to aid in AI PDDL development. This tool is primarily aimed at researchers and developers who need a visual representation of their planning problem so that they can make useful insights into the performance of their system, and also to naturally convey their system to others. It is an open-source tool which allows a user to quickly and easily visualise a target environment to generate the problem files and also to visualise a plan. It is particularly well suited to spatial planning problems. This paper will demonstrate vPlanSim on 2D and 3D planning problems. vPlanSim is based on a small and carefully considered set of dependencies such as VTK and PyQt. It can be set up on different platforms and compiled from source with minimal effort. The code is and maintained via a clear code review mechanism. We welcome contributions from the open-source community.
Speeding Up Search-Based Motion Planning using Expansion Delay Heuristics
Authors: Kaur Jasmeet , Chatterjee Ishani , Likhachev Maxim
Published: September 2022

Suboptimal search algorithms are a popular way to find solutions to planning problems faster by trading off solution optimality for search time. This is often achieved with the help of inadmissible heuristics. Prior work has explored ways to learn such inadmissible heuristics. However, it has focused on learning the heuristic value as an estimate of the cost to reach a goal. In this paper, we present a different approach that computes inadmissible heuristics by learning Expansion Delay for transitions in the state space. Expansion Delay is defined as the number of states expanded during the search between two consecutive states. Expansion Delay can be used as a measure of the depth of local minima regions i.e., regions where the heuristic(s) are weakly correlated with the true cost-to-goal (Vats, Narayanan and Likhachev 2017). Our key idea is to learn this measure in order to guide the search such that it reduces the total Expansion delay for reaching the goal and hence, avoid local minima regions in the state space. We analyze our method on 3D (x, y, theta) planning and Humanoid footstep planning. We find that the heuristics computed using our technique result in finding feasible plans faster.
Generalized Planning as Heuristic Search
Authors: Segovia-Aguas Javier , Jiménez Sergio , Jonsson Anders
Published: September 2022

Although heuristic search is one of the most successful approaches to classical planning, this planning paradigm does not apply straightforwardly to Generalized Planning (GP). Planning as heuristic search traditionally addresses the computation of sequential plans by searching in a grounded state-space. On the other hand, GP aims at computing algorithm-like plans that can branch and loop, and that generalize to a (possibly infinite) set of planning instances. This paper adapts the {\em planning as heuristic search} paradigm to the particularities of GP and presents the first native heuristic search approach to GP. First, the paper defines a novel GP solution space that is independent of the number of planning instances in a GP problem, and the size of these instances. Second, the paper defines several evaluation and heuristic functions, that do not require grounding, for guiding a combinatorial search in the GP solution space, making it possible to handle state variables with large numerical domains (e.g. integers). Lastly, the paper defines an algorithm for GP called Best-First Generalized Planning (BFGP), that implements a best-first search in the solution space guided by our evaluation/heuristic functions.
PLGRIM: Hierarchical Value Learning for Large-scale Exploration in Unknown Environments
Authors: Kim Sung-Kyun , Bouman Amanda , Salhotra Gautam , Fan David D. , Otsu Kyohei , Burdick Joel , Agha-mohammadi Ali-akbar
Published: September 2022

In order for an autonomous robot to efficiently explore an unknown environment, it must account for uncertainty in sensor measurements, hazard assessment, localization, and motion execution. Making decisions for maximal reward in a stochastic setting requires value learning and policy construction over a belief space, i.e., probability distribution over all possible robot-world states. However, belief space planning in a large spatial environment over long temporal horizons suffers from severe computational challenges. Moreover, constructed policies must safely adapt to unexpected changes in the belief at runtime. This work proposes a scalable value learning framework, PLGRIM (Probabilistic Local and Global Reasoning on Information roadMaps), that bridges the gap between (i) local, risk-aware resiliency and (ii) global, reward-seeking mission objectives. Leveraging hierarchical belief space planners with information-rich graph structures, PLGRIM addresses large-scale exploration problems while providing locally near-optimal coverage plans. We validate our proposed framework with high-fidelity dynamic simulations in diverse environments and on physical robots in Martian-analog lava tubes.
Operator-Potentials in Symbolic Search: From Forward to Bi-directional Search
Authors: Fišer Daniel , Torralba Álvaro , Hoffmann Jörg
Published: September 2022

Symbolic search using binary decision diagrams is a state-of-the-art technique for cost-optimal planning. Heuristic search in this context has been problematic as even a very informative heuristic can be detrimental in case it induces difficult-to-represent state partitionings. It was recently shown that operator-potential heuristics can address this issue in forward search by computing a numeric potential for each operator corresponding to the change of the heuristic value induced by that operator.

Forward search is, however, not the best known variant of symbolic search. Here we investigate the integration with backward and bi-directional search instead. We prove that forward search (distance-to-goal) operator-potential heuristics can be turned into backward search (distance-to-initial-state) heuristics elegantly in this context, by summing the backward search path operator-potentials with the initial state goal-distance estimate. We run exhaustive experiments on IPC benchmarks, showing that significant performance improvements can be obtained over symbolic forward search and other state-of-the-art techniques.
Debugging a Policy: Automatic Action-Policy Testing in AI Planning
Authors: Steinmetz Marcel , Fišer Daniel , Eniser Hasan Ferit , Ferber Patrick , Gros Timo P. , Heim Philippe , Höller Daniel , Schuler Xandra , Wüstholz Valentin , Christakis Maria , Hoffmann Jörg
Published: September 2022

Testing is a promising way to gain trust in neural action policies π. Previous work on policy testing in sequential decision making targeted environment behavior leading to failure conditions. But if the failure is unavoidable given that behavior, then π is not actually to blame. For a situation to qualify as a "bug" in π, there must be an alternative policy π' that does better. We introduce a generic policy testing framework based on that intuition. This raises the bug confirmation problem, deciding whether or not a state is a bug. We analyze the use of optimistic and pessimistic bounds for the design of test oracles approximating that problem. We contribute an implementation of our framework in classical planning, experimenting with several test oracles and with random-walk methods generating test states biased to poor policy performance and/or state novelty. We evaluate these techniques on policies π learned with ASNets. We find that they are able to effectively identify bugs in these π, and that our random-walk biases improve over uninformed baselines.
Distributed Fleet Management in Noisy Environments via Model-Predictive Control
Authors: Bøgh Simon , Gjøl Jensen Peter , Kristjansen Martin , Guldstrand Larsen Kim , Nyman Ulrik
Published: September 2022

We consider dynamic route planning for a fleet of Autonomous Mobile Robots (AMRs) doing fetch and carry tasks on a shared factory floor. In this paper, we propose Stochastic Work Graphs (SWG) as a formalism for capturing the semantics of such distributed and uncertain planning problems. We encode SWGs in the form of a Euclidean Markov Decision Process (EMDP) in the tool Uppaal Stratego, which employs Q-Learning to synthesize near-optimal plans. Furthermore, we deploy the tool in an online and distributed fashion to facilitate scalable, rapid replanning. While executing their current plan, each AMR generates a new plan incorporating updated information about the other AMRs positions and plans. We propose a two-layer Model Predictive Controller-structure (waypoint and station planning), each individually solved by the Q-learning-based solver. We demonstrate our approach using ARGoS3 large-scale robot simulation, where we simulate the AMR movement and observe an up to 27.5% improvement in makespan over a greedy approach to planning. To do so, we have implemented the full software stack, translating observations into SWGs and solving those with our proposed method. In addition, we construct a benchmark platform for comparing planning techniques on a reasonably realistic physical simulation and provide this under the MIT open-source license.
TempAMLSI: Temporal Action Model Learning Based on STRIPS Translation
Authors: Grand Maxence , Pellier Damien , Fiorino Humbert
Published: September 2022

Hand-encoding PDDL domains is generally considered difficult, tedious and error-prone. The difficulty is even greater when temporal domains have to be encoded. Indeed, actions have a duration and their effects are not instantaneous. In this paper, we present TempAMLSI, an algorithm based on the AMLSI approach to learn temporal domains. TempAMLSI is the first approach able to learn temporal domains with single hard envelopes, and TempAMLSI is the first approach able to deal with both partial and noisy observations. We show experimentally that TempAMLSI learns accurate temporal domains, i.e., temporal domains that can be used without human proofreading to solve new planning problems with different forms of action concurrency.
Survey of mite species of tea plantations in Rize
Authors: Diler Heval , Yazıcı Gülten , Saçtı Zühal , Yücel Cenk , Barış Aydemir
Published: September 2022

This study was carried out between 2017-2019 to determine the mite species found in the tea gardens of Rize province (Türkiye). During the study, surveys in 9 districts (Ardeşen, Çayeli, Derepazarı, Fındıklı, Güneysu, İyidere, Kalkandere, Centre and Pazar) were carried out between May and September with the random sampling method, corresponding to the tea plant's 1st, 2nd and 3rd shoot period. Studies were conducted in 73 tea gardens in 9 districts in the first shoot period, 107 tea gardens in the 2nd shoot period and in 97 tea gardens in the 3rd shoot period and plant samples were taken. At the end of the study, Polyphagotarsonemus latus (Banks) (Acari: Tarsonemidae) was the most common and highest number detected species with 49.7%. Other mites identified are Calacarus carinatus (Green) (Prostigmata: Eriophyidae) with 24.3%, Brevipalpus phoenicis (Geijskes) (Prostigmata: Tenuipalpidae) with 19.9%, Tydeus californicus (Banks) (Prostigmata: Tydeidae) with 17.1%, Neoseiulus californicus (Oudemans) (Mesostigmata: Phytoseiidae) with 5.9%, Czenspinskia transversostriata (Oudemans) (Acari: Winterschmidtiidae) with 2.9%, Tuckerella sp. (Prostigmata: Tuckerellidae) with 1.8%, and Oribatida species, respectively. The mites found in this study have been completed in a way that integrates with previous studies.
Funciones narrativas de la voz en la serie Narcos de Netflix
Authors: Cardona Cano César , Heredia Ruiz Verónica , Alzate Giraldo Alejandro
Published: September 2022

Este artículo analiza la narración de la serie Narcos (2015), a partir de la voz y el acento de los personajes y su relación con el concepto de televisión transnacional y global. Con una metodología mixta y un diseño explicativo secuencial evalúa las voces del capítulo de estreno de la primera temporada, a partir de variables como la función narrativa, el idioma y el acento, con el fin de establecer una relación con los personajes, país de procedencia de los actores, y la apuesta de Netflix por construir una serie transnacional y global.
Flotation of weathered coal discards using a HIP W/O emulsion binder and kerosene
Authors: Mashaba P.M. , Bada S.O.
Published: September 2022

An advanced high internal phase water-in-oil (HIP W/O) emulsion binder and flotation with an internal dispersed water phase of 95 vol% was used to beneficiate South African weathered coal from discard dumps. Five coal samples with calorific values ranged from 8.48 MJ/kg to 20.94 MJ/kg were utilized. The performance of the HIP W/O emulsion was compared to that of kerosene in terms of the physico-chemical properties of the clean coal products. A 5 kg/t emulsion binder addition produced a high-quality coal concentrate with 11.76% ash and a heating value of 28.61 MJ/kg from a sample containing 29.34% ash. Using the same dose and coal sample, kerosene resulted in a clean coal concentrate with 17.47% ash and a calorific value of 26.42 MJ/kg. Further samples were also beneficiated using 5 kg/t of W/O emulsion. The highest increase in calorific value was achieved from the beneficiation of a sample containing 48.71% ash, from 8.48 MJ/kg to 18.16 MJ/kg. It has been established that emulsion binders can upgrade coal samples dumped many decades ago.
Powdery Mildew Detection in Hazelnut with Deep Learning
Authors: BOYAR Tülin , YILDIZ Kazım
Published: September 2022

Hazelnut cultivation is widely practiced in our country. One of the major problems in hazelnut cultivation is powdery mildew disease on hazelnut tree leaves. In this study, the early detection of powdery mildew disease with the YOLO model based on machine learning was tested on a unique data set. Object detection on the image, which is widely applied in the detection of plant diseases, has been applied for the detection of powdery mildew diseases. According to the results obtained, it has been seen that powdery mildew disease can be detected on the image. In the network trained with the Yolov5 model, diseased areas were detected with 95% accuracy in leaf images containing many diseases. Detection of healthy leaves, on the other hand, was tried on images with complex backgrounds and could detect more than one leaf on an image with 85% accuracy. The Yolov5 model, which has been used in many studies for disease detection on plant leaves, also gave effective results for the detection of powdery mildew disease on hazelnut leaves. Early detection of powdery mildew with a method based on machine learning; will stop the possible spread of disease; It will increase the efficiency of hazelnut production by preventing the damage of hazelnut producers.
Synthesis and Characterization of New Ferrocenyl and 3-methoxytolyl Dithiophosphonates
Authors: SAĞLAM Ertuğrul Gazi , BULAT Elif , YILDIRIM Ayşe
Published: September 2022

In this study, perthiophosphonic acid anhydrides which are previously known (2,4-bis(3-methoxytolyl)-1,3-dithia-2,4-diphosphetane-2,4-disulfide ([(3-methoxytolyl)P(S)S)2], SAV-B1 reagent and 2,4-bis(ferrocenyl)-1,3,2,4-dithiadiphosphetane-2,4-disulfide [(ferrocenyl)P(S)S)2], FcLR, Ferrocenyl Lawesson's Reagent) react with alcohols to obtain new dithiophosphonic acids (3-methoxytolyl=(CH3)(OCH3)C6H3- and Xn=alkyl group (R) for SAV-B1, HXn; R1=3-pentyl-, HX1; R2=2-butyl-, HX2; R3=2-pentyl-, HX3 and Fc=Ferrocenyl- and Yn=allkyl or aryl group (R’) for FcLR, HYn; R’1=4-tert-cyclohexyl-, HY1; R’2=2-phenyl-1-propyl-, HY2). These acids were then converted to ammonium salts ([NH4][Xn], [NH4][Yn]). The structures of ammonium salts were characterized by mass-(ESI), FT-IR, 1H-, 13C- and 31P-NMR spectroscopy and elemental analysis.