20 papers · ranked by Valyu relevance
Damien Pellier, Bruno Bouzy, Marc Métivier
In this paper, we introduce a new heuristic search algorithm based on mean values for real-time planning, called MHSP. It consists in associating the principles of UCT, a bandit-based algorithm which gave very good results in computer games, and especially in Computer Go, with heuristic search in order to obtain a…
Jefferson Silveira, Kléber Cabral, Sidney Givigi, Joshua A. Marshall
— This paper proposes the Real-Time Fast Marching Tree (RT-FMT), a real-time planning algorithm that features local and global path generation, multiple-query planning, and dynamic obstacle avoidance. During the search, RT-FMT quickly looks for the global solution and, in the meantime, generates local paths that can be…
Runzhe Liang, Rishi Veerapaneni, Daniel Harabor, Jiaoyang Li + 1 more
'Maxim Likhachev'] The vast majority of Multi-Agent Path Finding (MAPF) methods with completeness guarantees require planning full horizon paths. However, planning full horizon paths can take too long and be impractical in real-world applications. Instead, real-time planning and execution, which only allows the planner…
Bernardo Martinez Rocamora Jr., Guilherme A. S. Pereira, Ramon Barber
This paper presents a parallel motion planner for mobile robots and autonomous vehicles based on lattices created in the sensor space of planar range finders. The planner is able to compute paths in a few milliseconds, thus allowing obstacle avoidance in real time. The proposed sensor-space lattice (SSLAT) motion…
Andrea Traldi, Francesco Bruschetti, Marco Robol, Marco Roveri + 1 more
'Paolo Giorgini'] Abstract: The BDI model proved to be effective for developing applications requiring high-levels of autonomy and to deal with the complexity and unpredictability of real-world scenarios. The model, however, has significant limitations in reacting and handling contingencies within the given real-time…
Carlos Calvo Tapia, José Antonio Villacorta-Atienza, Gonzalo Aparicio-Rodríguez, Paloma Manubens + 3 more
Time compaction theory is a general framework explaining how a brain can efficiently deal with dynamic situations occurring in, e.g., sports games. It involves a geometric representation of the time dimension, which enables effective learning and strategic action planning. The theory has recently received experimental…
Pablo Fernandez Velasco, Eva-Maria Griesbauer, Iva Brunec, Jeremy Morley + 3 more
Efficient planning is a distinctive hallmark of intelligence in humans, who routinely make rapid inferences over complex world contexts. However, studies investigating how humans accomplish this tend to focus on naive participants engaged in simplistic tasks with small state-spaces, which do not reflect the intricacy…
Anders Lager, Giacomo Spampinato, Alessandro V. Papadopoulos, Thomas Nolte
'Thomas Nolte'] Modern industrial robots are increasingly deployed in dynamic environments, where unpredictable events are expected to impact the robot’s operation. Under these conditions, runtime task replanning is required to avoid failures and unnecessary stops, while keeping up productivity. Task replanning is a…
Fatmaalzahraa A Aboalasaad, Leonie Lambertz, Chiara Mastrogiuseppe, Rubén Moreno-Bote
In our everyday lives, we continually need to commit to courses of future action even when direct feedback is not available. The expected reward of an action depends not only on a single decision but on a sequence of interdependent choices that will happen in the future. Admittedly, most decisions we make concern…
Alan Lindsay, Andrés A. Ramírez-Duque, Bart Craenen, David A. Robb + 4 more
The task of supporting a human operator to understand generated plans, and to explore the plan space, are important problems in automated planning. In this work, we consider the problem of plan explainability and plan space exploration in underwater autonomous vehicle missions. In this context, concepts that are useful…
Nicola Gigante
- R Department of Mathematics, Computer Science, and Physics Università degli Studi di Udine Via delle Scienze 208 Udine 33100 (UD), Italy - [ nicola.gigante@uniud.it - Submitted to referees on 31 October 2018 - Ë Approved on 16 January 2019 Final revision submitted on 30 January 2019 - An up-to-date version of this…
Nicolo Ferron, Gabriele Manduchi, Bruno Goncalves
One of the most important applications of sensors is feedback control, in which an algorithm is applied to data that are collected from sensors in order to drive system actuators and achieve the desired outputs of the target plant. One of the most challenging applications of this control is represented by magnetic…
Momchil S. Tomov, Samyukta Yagati, Agni Kumar, Wanqian Yang + 1 more
We propose that humans spontaneously organize environments into clusters of states that support hierarchical planning, enabling them to tackle challenging problems by breaking them down into sub-problems at various levels of abstraction. People constantly rely on such hierarchical presentations to accomplish tasks big…
Eva-Maria Griesbauer, Pablo Fernandez Velasco, Antoine Coutrot, Jan M. Wiener + 4 more
Humans show an impressive ability to plan over complex situations and environments. A classic approach to explaining such planning has been tree-search algorithms which search through alternative state sequences for the most efficient path through states. However, this approach fails when the number of states is large…
Dogu Baran Aydogan, Victor H. Souza, Renan H. Matsuda, Pantelis Lioumis + 1 more
State-of-the-art navigated transcranial magnetic stimulation (nTMS) systems can display the TMS coil position relative to the structural magnetic resonance image (MRI) of the subject’s brain and calculate the induced electric field. However, the local effect of TMS propagates via the white-matter network to different…
Toon Albers, Elena Lazovik, Mostafa Hadadian Nejad Yousefi, Alexander Lazovik
Distributed data processing systems have become the standard means for big data analytics. These systems are based on processing pipelines where operations on data are performed in a chain of consecutive steps. Normally, the operations performed by these pipelines are set at design time, and any changes to their…
Pablo Quijano Velasco, Kedar Hippalgaonkar, Balamurugan Ramalingam
The discovery of optimal conditions of chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and Design of Experiments where one…
Authors not listed
Computer-aided synthesis planning aims to identify viable synthetic routes from a target compound to readily available building blocks by iteratively decomposing molecules into smaller precursors. Self-play search algorithms, trained with simulated experience, reach state-of-the-art performance. However, these methods…
Riley Hickman, Malcolm Sim, Sergio Pablo-García, Ivan Woolhouse + 6 more
Self-driving laboratories (SDLs) are next-generation research and development platforms for closed-loop, autonomous experimentation that combine ideas from artificial intelligence, robotics, and high-performance computing. A critical component of SDLs is the decision-making algorithm used to prioritize experiments to…
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We present RetroSynFormer, a novel approach to multi-step retrosynthesis planning. Here, we express the task of iteratively breaking down a compound into building blocks as a sequence-modeling problem and train a model based on the Decision Transformer. The synthesis routes are generated by iteratively predicting…