Time-Bounded Best-First Search for Reversible and Non-reversible Search Graphs

Time-Bounded A* is a real-time, single-agent, deterministic search algorithm that expands states of a graph in the same order as A* does, but that unlike A* interleaves search and action execution. Known to outperform state-of-the-art real-time search algorithms based on Korf's Learning Real-Time A* (LRTA*) in some benchmarks, it has not been studied in detail and is sometimes not considered as a ``true'' real-time search algorithm since it fails in non-reversible problems even it the goal is still reachable from the current state. In this paper we propose and study Time-Bounded Best-First Search (TB(BFS)) a straightforward generalization of the time-bounded approach to any best-first search algorithm. Furthermore, we propose Restarting Time-Bounded Weighted A* (TB_R(WA*)), an algorithm that deals more adequately with non-reversible search graphs, eliminating ``backtracking moves'' and incorporating search restarts and heuristic learning. In non-reversible problems we prove that TB(BFS) terminates and we deduce cost bounds for the solutions returned by Time-Bounded Weighted A* (TB(WA*)), an instance of TB(BFS). Furthermore, we prove TB_R(WA*), under reasonable conditions, terminates. We evaluate TB(WA) in both grid pathfinding and the 15-puzzle. In addition, we evaluate TB_R(WA*) on the racetrack problem. We compare our algorithms to LSS-LRTWA*, a variant of LRTA* that can exploit lookahead search and a weighted heuristic. A general observation is that the performance of both TB(WA*) and TB_R(WA*) improves as the weight parameter is increased. In addition, our time-bounded algorithms almost always outperform LSS-LRTWA* by a significant margin.

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Reconnection with the Ideal Tree: A New Approach to Real-Time Search

Journal of Artificial Intelligence Research ◽

10.1613/jair.4292 ◽

2014 ◽

Vol 50 ◽

pp. 235-264 ◽

Cited By ~ 1

Author(s):

N. Rivera ◽

L. Illanes ◽

J. A. Baier ◽

C. Hernandez

Keyword(s):

Real Time ◽

Search Algorithm ◽

Single Agent ◽

Time Constraints ◽

Search Problems ◽

Planning Time ◽

Current State ◽

Actual Environment ◽

Order Of Magnitude ◽

Blind Search

Many applications, ranging from video games to dynamic robotics, require solving single-agent, deterministic search problems in partially known environments under very tight time constraints. Real-Time Heuristic Search (RTHS) algorithms are specifically designed for those applications. As a subroutine, most of them invoke a standard, but bounded, search algorithm that searches for the goal. In this paper we present FRIT, a simple approach for single-agent deterministic search problems under tight constraints and partially known environments that unlike traditional RTHS does not search for the goal but rather searches for a path that connects the current state with a so-called ideal tree T . When the agent observes that an arc in the tree cannot be traversed in the actual environment, it removes such an arc from T and then carries out a reconnection search whose objective is to find a path between the current state and any node in T . The reconnection search is done using an algorithm that is passed as a parameter to FRIT. If such a parameter is an RTHS algorithm, then the resulting algorithm can be an RTHS algorithm. We show, in addition, that FRIT may be fed with a (bounded) complete blind-search algorithm. We evaluate our approach over grid pathfinding benchmarks including game maps and mazes. Our results show that FRIT, used with RTAA*, a standard RTHS algorithm, outperforms RTAA* significantly; by one order of magnitude under tight time constraints. In addition, FRIT(daRTAA*) substantially outperforms daRTAA*, a state-of-the-art RTHS algorithm, usually obtaining solutions 50% cheaper on average when performing the same search effort. Finally, FRIT(BFS), i.e., FRIT using breadth-first-search, obtains best-quality solutions when time is limited compared to Adaptive A* and Repeated A*. Finally we show that Bug2, a pathfinding-specific navigation algorithm, outperforms FRIT(BFS) when planning time is extremely limited, but when given more time, the situation reverses.

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Real Time Simulation for High Fidelity Multibody Dynamics and Mechatronic Systems

Volume 4B: Dynamics, Vibration, and Control ◽

10.1115/imece2014-39994 ◽

2014 ◽

Author(s):

William Prescott

Keyword(s):

Real Time ◽

Multibody Dynamics ◽

Large Scale ◽

State Of The Art ◽

High Fidelity ◽

Implicit Integration ◽

Mechatronic Systems ◽

Vehicle Simulation ◽

Current State ◽

Time Simulation

This paper will investigate the use of large scale multibody dynamics (MBD) models for real-time vehicle simulation. Current state of the art in the real-time solution of vehicle uses 15 degree of freedom models, but there is a need for higher-fidelity systems. To increase the fidelity of models uses this paper will propose the use of the following techniques: implicit integration, parallel processing and co-simulation in a real-time environment.

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HESML: a real-time semantic measures library for the biomedical domain with a reproducible survey

BMC Bioinformatics ◽

10.1186/s12859-021-04539-0 ◽

2022 ◽

Vol 23 (1) ◽

Author(s):

Juan J. Lastra-Díaz ◽

Alicia Lara-Clares ◽

Ana Garcia-Serrano

Keyword(s):

Real Time ◽

Semantic Similarity ◽

Similarity Measure ◽

Shortest Path ◽

State Of The Art ◽

Biomedical Domain ◽

Snomed Ct ◽

Shortest Path Algorithm ◽

Semantic Similarity Measure ◽

Current State

Abstract Background Ontology-based semantic similarity measures based on SNOMED-CT, MeSH, and Gene Ontology are being extensively used in many applications in biomedical text mining and genomics respectively, which has encouraged the development of semantic measures libraries based on the aforementioned ontologies. However, current state-of-the-art semantic measures libraries have some performance and scalability drawbacks derived from their ontology representations based on relational databases, or naive in-memory graph representations. Likewise, a recent reproducible survey on word similarity shows that one hybrid IC-based measure which integrates a shortest-path computation sets the state of the art in the family of ontology-based semantic measures. However, the lack of an efficient shortest-path algorithm for their real-time computation prevents both their practical use in any application and the use of any other path-based semantic similarity measure. Results To bridge the two aforementioned gaps, this work introduces for the first time an updated version of the HESML Java software library especially designed for the biomedical domain, which implements the most efficient and scalable ontology representation reported in the literature, together with a new method for the approximation of the Dijkstra’s algorithm for taxonomies, called Ancestors-based Shortest-Path Length (AncSPL), which allows the real-time computation of any path-based semantic similarity measure. Conclusions We introduce a set of reproducible benchmarks showing that HESML outperforms by several orders of magnitude the current state-of-the-art libraries in the three aforementioned biomedical ontologies, as well as the real-time performance and approximation quality of the new AncSPL shortest-path algorithm. Likewise, we show that AncSPL linearly scales regarding the dimension of the common ancestor subgraph regardless of the ontology size. Path-based measures based on the new AncSPL algorithm are up to six orders of magnitude faster than their exact implementation in large ontologies like SNOMED-CT and GO. Finally, we provide a detailed reproducibility protocol and dataset as supplementary material to allow the exact replication of all our experiments and results.

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DisCountNet: Discriminating and Counting Network for Real-Time Counting and Localization of Sparse Objects in High-Resolution UAV Imagery

Remote Sensing ◽

10.3390/rs11091128 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1128 ◽

Cited By ~ 10

Author(s):

Maryam Rahnemoonfar ◽

Dugan Dobbs ◽

Masoud Yari ◽

Michael J. Starek

Keyword(s):

High Resolution ◽

Real Time ◽

State Of The Art ◽

Sparse Matrix ◽

Aerial Images ◽

Heat Map ◽

High Resolution Image ◽

Current State ◽

Density Map ◽

Distinct Features

Recent deep-learning counting techniques revolve around two distinct features of data—sparse data, which favors detection networks, or dense data where density map networks are used. Both techniques fail to address a third scenario, where dense objects are sparsely located. Raw aerial images represent sparse distributions of data in most situations. To address this issue, we propose a novel and exceedingly portable end-to-end model, DisCountNet, and an example dataset to test it on. DisCountNet is a two-stage network that uses theories from both detection and heat-map networks to provide a simple yet powerful design. The first stage, DiscNet, operates on the theory of coarse detection, but does so by converting a rich and high-resolution image into a sparse representation where only important information is encoded. Following this, CountNet operates on the dense regions of the sparse matrix to generate a density map, which provides fine locations and count predictions on densities of objects. Comparing the proposed network to current state-of-the-art networks, we find that we can maintain competitive performance while using a fraction of the computational complexity, resulting in a real-time solution.

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Using 3D Convolutional Neural Networks for Real-time Detection of Soccer Events

International Journal of Semantic Computing ◽

10.1142/s1793351x2140002x ◽

2021 ◽

Vol 15 (02) ◽

pp. 161-187

Author(s):

Olav A. Nergård Rongved ◽

Steven A. Hicks ◽

Vajira Thambawita ◽

Håkon K. Stensland ◽

Evi Zouganeli ◽

...

Keyword(s):

Neural Networks ◽

Real Time ◽

Convolutional Neural Networks ◽

Event Detection ◽

State Of The Art ◽

Time Estimation ◽

High Recall ◽

Current State ◽

Ablation Study ◽

Different Parts

Developing systems for the automatic detection of events in video is a task which has gained attention in many areas including sports. More specifically, event detection for soccer videos has been studied widely in the literature. However, there are still a number of shortcomings in the state-of-the-art such as high latency, making it challenging to operate at the live edge. In this paper, we present an algorithm to detect events in soccer videos in real time, using 3D convolutional neural networks. We test our algorithm on three different datasets from SoccerNet, the Swedish Allsvenskan, and the Norwegian Eliteserien. Overall, the results show that we can detect events with high recall, low latency, and accurate time estimation. The trade-off is a slightly lower precision compared to the current state-of-the-art, which has higher latency and performs better when a less accurate time estimation can be accepted. In addition to the presented algorithm, we perform an extensive ablation study on how the different parts of the training pipeline affect the final results.

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Verification and Parameter Synthesis for Real-Time Programs using Refinement of Trace Abstraction*

Fundamenta Informaticae ◽

10.3233/fi-2021-1997 ◽

2021 ◽

Vol 178 (1-2) ◽

pp. 31-57

Author(s):

Franck Cassez ◽

Peter Gjøl Jensen ◽

Kim Guldstrand Larsen

Keyword(s):

Real Time ◽

State Of The Art ◽

Timed Automata ◽

Robustness Analysis ◽

Hybrid Automata ◽

Timed Systems ◽

Current State ◽

Parameter Synthesis ◽

Series Of Experiments ◽

Time Systems

We address the safety verification and synthesis problems for real-time systems. We introduce real-time programs that are made of instructions that can perform assignments to discrete and real-valued variables. They are general enough to capture interesting classes of timed systems such as timed automata, stopwatch automata, time(d) Petri nets and hybrid automata. We propose a semi-algorithm using refinement of trace abstractions to solve both the reachability verification problem and the parameter synthesis problem for real-time programs. All of the algorithms proposed have been implemented and we have conducted a series of experiments, comparing the performance of our new approach to state-of-the-art tools in classical reachability, robustness analysis and parameter synthesis for timed systems. We show that our new method provides solutions to problems which are unsolvable by the current state-of-the-art tools.

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Online Bridged Pruning for Real-Time Search with Arbitrary Lookaheads

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/72 ◽

2017 ◽

Author(s):

Carlos Hernandez ◽

Adi Botea ◽

Jorge A. Baier ◽

Vadim Bulitko

Keyword(s):

Real Time ◽

Recent Progress ◽

State Of The Art ◽

Search Algorithm ◽

Search Space ◽

Poor Quality ◽

Search Algorithms ◽

Arbitrary Size ◽

Order Of Magnitude ◽

And Robotics

Real-time search algorithms are relevant to time-sensitive decision-making domains such as video games and robotics. In such settings, the agent is required to decide on each action under a constant time bound, regardless of the search space size. Despite recent progress, poor-quality solutions can be produced mainly due to state re-visitation. Different techniques have been developed to reduce such a re-visitation with state pruning showing promise. In this paper, we propose a novel pruning approach applicable to the wide class of real-time search algorithms. Given a local search space of arbitrary size, our technique aggressively prunes away all states in its interior, possibly adding new edges to maintain the connectivity of the search space frontier. An experimental evaluation shows that our pruning often improves the performance of a base real-time search algorithm by over an order of magnitude. This allows our implemented system to outperform state-of-the-art real-time search algorithms used in the evaluation.

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Scaling up Heuristic Planning with Relational Decision Trees

Journal of Artificial Intelligence Research ◽

10.1613/jair.3231 ◽

2011 ◽

Vol 40 ◽

pp. 767-813 ◽

Cited By ~ 9

Author(s):

T. De la Rosa ◽

S. Jimenez ◽

R. Fuentetaja ◽

D. Borrajo

Keyword(s):

State Of The Art ◽

Search Algorithm ◽

Learning Task ◽

Classification Task ◽

Evaluation Functions ◽

Current State ◽

Classification Tool ◽

Search Control ◽

Planning Problems ◽

Relational Classification

Current evaluation functions for heuristic planning are expensive to compute. In numerous planning problems these functions provide good guidance to the solution, so they are worth the expense. However, when evaluation functions are misguiding or when planning problems are large enough, lots of node evaluations must be computed, which severely limits the scalability of heuristic planners. In this paper, we present a novel solution for reducing node evaluations in heuristic planning based on machine learning. Particularly, we define the task of learning search control for heuristic planning as a relational classification task, and we use an off-the-shelf relational classification tool to address this learning task. Our relational classification task captures the preferred action to select in the different planning contexts of a specific planning domain. These planning contexts are defined by the set of helpful actions of the current state, the goals remaining to be achieved, and the static predicates of the planning task. This paper shows two methods for guiding the search of a heuristic planner with the learned classifiers. The first one consists of using the resulting classifier as an action policy. The second one consists of applying the classifier to generate lookahead states within a Best First Search algorithm. Experiments over a variety of domains reveal that our heuristic planner using the learned classifiers solves larger problems than state-of-the-art planners.

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I2DNet - Design and real-time evaluation of an appearance-based gaze estimation system

Journal of Eye Movement Research ◽

10.16910/jemr.14.4.2 ◽

2021 ◽

Vol 14 (4) ◽

Author(s):

L R D Murthy ◽

Siddhi Brahmbhatt ◽

Somnath Arjun ◽

Pradipta Biswas

Keyword(s):

Real Time ◽

User Study ◽

State Of The Art ◽

Estimation Accuracy ◽

Point Estimate ◽

Gaze Estimation ◽

Model Based ◽

Current State ◽

Learning Techniques ◽

Estimation System

Gaze estimation problem can be addressed using either model-based or appearance-based approaches. Model-based approaches rely on features extracted from eye images to fit a 3D eye-ball model to obtain gaze point estimate while appearance-based methods attempt to directly map captured eye images to gaze point without any handcrafted features. Recently, availability of large datasets and novel deep learning techniques made appearance-based methods achieve superior accuracy than model-based approaches. However, many appearance-based gaze estimation systems perform well in within-dataset validation but fail to provide the same degree of accuracy in cross-dataset evaluation. Hence, it is still unclear how well the current state-of-the-art approaches perform in real-time in an interactive setting on unseen users. This paper proposes I2DNet, a novel architecture aimed to improve subject-independent gaze estimation accuracy that achieved a state-of-the-art 4.3 and 8.4 degree mean angle error on the MPIIGaze and RT-Gene datasets respectively. We have evaluated the proposed system as a gaze-controlled interface in real-time for a 9-block pointing and selection task and compared it with Webgazer.js and OpenFace 2.0. We have conducted a user study with 16 participants, and our proposed system reduces selection time and the number of missed selections statistically significantly compared to other two systems.

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