scholarly journals Plan recognition in dynamic 2-D environments

2021 ◽  
Author(s):  
Joshua Gross
Keyword(s):  
Moving Objects ◽  
Finite Automata ◽  
Plan Recognition ◽  
Situation Calculus ◽  
Logical Language ◽  
Left Turns ◽  
High Level ◽  
Planning Problems ◽  
Logical Formalism ◽  
Right Turns

We look at the relatively unexplored problem of plan recognition applied to motion in 2-D environments where all moving objects are modelled as circles. Golog is a well-known high level logical language for solving planning problems and specifying agent controllers. Few studies have applied Golog to plan recognition. We use some of the features of this language, but its standard interpreter is adapted to solving plan recognition problems. This thesis makes several other contributions. First, plan recognition procedures are formulated as finite automata and expressed as Golog programs. Second, we elaborate a logical formalism for reasoning about depth and motion from an observer's viewpoint. We not only expand on this situation calculus based formalism, but also apply it to tackle plan recognition problems in the traffic domain. The proposed approach is implemented and thoroughly tested on recognizing simple behaviours such as left turns, right turns, and overtaking.

scholarly journals Plan recognition in dynamic 2-D environments

2021 ◽  
Author(s):  
Joshua Gross
Keyword(s):  
Moving Objects ◽  
Finite Automata ◽  
Plan Recognition ◽  
Situation Calculus ◽  
Logical Language ◽  
Left Turns ◽  
High Level ◽  
Planning Problems ◽  
Logical Formalism ◽  
Right Turns

We look at the relatively unexplored problem of plan recognition applied to motion in 2-D environments where all moving objects are modelled as circles. Golog is a well-known high level logical language for solving planning problems and specifying agent controllers. Few studies have applied Golog to plan recognition. We use some of the features of this language, but its standard interpreter is adapted to solving plan recognition problems. This thesis makes several other contributions. First, plan recognition procedures are formulated as finite automata and expressed as Golog programs. Second, we elaborate a logical formalism for reasoning about depth and motion from an observer's viewpoint. We not only expand on this situation calculus based formalism, but also apply it to tackle plan recognition problems in the traffic domain. The proposed approach is implemented and thoroughly tested on recognizing simple behaviours such as left turns, right turns, and overtaking.

Declarative Planning and Knowledge Representation in an Action Language

2011 ◽  
pp. 192-221 ◽  
Author(s):  
Thomas Eiter ◽  
Wolfgang Faber ◽  
Gerald Pfeifer
Keyword(s):  
Knowledge Representation ◽  
Logic Programming ◽  
Planning System ◽  
Epistemic State ◽  
Action Languages ◽  
Knowledge States ◽  
Action Language ◽  
High Level ◽  
Planning Problems ◽  
Complex Planning

This chapter introduces planning and knowledge representation in the declarative action language K. Rooted in the area of Knowledge Representation & Reasoning, action languages like K allow the formalization of complex planning problems involving non-determinism and incomplete knowledge in a very flexible manner. By giving an overview of existing planning languages and comparing these against our language, we aim on further promoting the applicability and usefulness of high-level action languages in the area of planning. As opposed to previously existing languages for modeling actions and change, K adopts a logic programming view where fluents representing the epistemic state of an agent might be true, false or undefined in each state. We will show that this view of knowledge states can be fruitfully applied to several well-known planning domains from the literature as well as novel planning domains. Remarkably, K often allows to model problems more concisely than previous action languages. All the examples given can be tested in an available implementation, the DLVK planning system.

On the Problem of Moving Objects With Autonomous Robots: A Unifying High-Level Planning Approach

2016 ◽  
Vol 1 (1) ◽  
pp. 469-476 ◽  
Author(s):  
Hamal Marino ◽  
Mirko Ferrati ◽  
Alessandro Settimi ◽  
Carlos Rosales ◽  
Marco Gabiccini
Keyword(s):  
Moving Objects ◽  
Autonomous Robots ◽  
Planning Approach ◽  
High Level

scholarly journals Swimmer’s Head Detection Based on a Contrario and Scaled Composite JTC Approaches

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
D. Benarab ◽  
T. Napoléon ◽  
A. Alfalou ◽  
A. Verney ◽  
P. Hellard
Keyword(s):  
Moving Objects ◽  
Tracking System ◽  
Random Noise ◽  
Region Of Interest ◽  
Reference Image ◽  
Correlation Technique ◽  
Head Detection ◽  
Linear Transform ◽  
High Level ◽  
Direct Linear Transform

In order to accompany the swimming coaches in evaluating high-level swimmers, we developed a prototype for instantaneous speed estimation. To achieve this, we proposed and validated, in a previous work, a swimmer tracking system based on data fusion. However, the initialization phase is done manually, and our aim, in this paper, is to automate this process. First, we propose a region of interest localization module that allows the detection of the first appearance of the swimmer in the lane as well as the restriction of the region of interest around him. This module is based on the method a contrario which consists of modeling the random noise corresponding to the water and detecting the structured movement relative to the swimmer motion. To do that, we calibrate the pool using DLT (Direct Linear Transform) technique, extract the concerned lane, apply the frame difference approach to detect the moving objects, and then decompose the lane into blocs and classify them into swimmer motion or noise. Second, in order to detect the swimmer’s head, we propose the Scaled Composite JTC which is based on the NL-JTC correlation technique. The input plane of this latter includes a target and a reference image. The first is the region of interest detected by the method a contrario. The second consists of a Scaled Composite Reference. The tests conducted on real video sequences of French swimming championships (Limoges 2015) showed very good results in terms of region of interest localization and swimmer’s head detection which allows a reliable initialization for the tracking system.

Optimal Location of U-Turn Median Openings on Roadways

2003 ◽  
Vol 1847 (1) ◽  
pp. 36-41 ◽  
Author(s):  
Huaguo Zhou ◽  
Peter Hsu ◽  
Jian John Lu ◽  
John E. Wright
Keyword(s):  
Optimal Location ◽  
Traffic Operations ◽  
Average Delay ◽  
Working Model ◽  
Left Turns ◽  
State And Local ◽  
Median Opening ◽  
Transportation Agencies ◽  
Right Turns

Many state and local transportation agencies have considered using U-turns as alternatives to direct left turns from driveways or side streets. Median designs are used that prohibit left turns onto the facility and mid-block U-turn median openings to accommodate diverted left turns from side streets or driveways. The location of these U-turn median openings has a great impact on the operations of U-turns. Traffic operations (weaving and delay) for right turns followed by U-turn movements on urban or suburban multilane roadways were analyzed. A working model was developed to guide the location of U-turn median openings by minimizing the average delay for U-turn movements. A case study demonstrates the operational and safety benefits of optimal U-turn median opening location.

scholarly journals Planning with preferences using logic programming

2006 ◽  
Vol 6 (5) ◽  
pp. 559-607 ◽  
Author(s):  
TRAN CAO SON ◽  
ENRICO PONTELLI
Keyword(s):  
Logic Programming ◽  
Answer Set Programming ◽  
Planning Problem ◽  
Declarative Language ◽  
High Level ◽  
Planning Problems ◽  
Answer Set

We present a declarative language, ${\cal PP}$, for the high-level specification of preferences between possible solutions (or trajectories) of a planning problem. This novel language allows users to elegantly express non-trivial, multi-dimensional preferences and priorities over such preferences. The semantics of ${\cal PP}$ allows the identification of most preferred trajectories for a given goal. We also provide an answer set programming implementation of planning problems with ${\cal PP}$ preferences.

scholarly journals A Probabilistic Extension of Action Language

2018 ◽  
Vol 18 (3-4) ◽  
pp. 607-622 ◽  
Author(s):  
JOOHYUNG LEE ◽  
YI WANG
Keyword(s):  
Probabilistic Reasoning ◽  
Transition Systems ◽  
Action Language ◽  
High Level ◽  
Planning Problems ◽  
Probabilistic Diagnosis ◽  
Answer Set

AbstractWe present a probabilistic extension of action language${\cal BC}$+$. Just like${\cal BC}$+$is defined as a high-level notation of answer set programs for describing transition systems, the proposed language, which we callp${\cal BC}$+$, is defined as a high-level notation of LPMLNprograms—a probabilistic extension of answer set programs. We show how probabilistic reasoning about transition systems, such as prediction, postdiction, and planning problems, as well as probabilistic diagnosis for dynamic domains, can be modeled inp${\cal BC}$+$and computed using an implementation of LPMLN.

scholarly journals The Nondeterministic Situation Calculus

2021 ◽  
Author(s):  
Giuseppe De Giacomo ◽  
Yves Lespérance
Keyword(s):  
Situation Calculus ◽  
Simple Extension ◽  
Program Execution ◽  
Frame Problem ◽  
Atomic Actions ◽  
High Level ◽  
The Frame Problem ◽  
Standard Situation ◽  
Nondeterministic Actions

The standard situation calculus assumes that atomic actions are deterministic. But many domains involve nondeterministic actions, with problems such as fully observable nondeterministic (FOND) planning and high-level program execution requiring solutions. Various approaches have been proposed to accommodate nondeterminism on top of the standard situation calculus language, for instance by introducing nondeterministic programs as in Golog and ConGolog. But a key problem in these approaches is that they don’t clearly distinguish between choices that can be made by the agent and choices that are made by the environment, i.e., angelic vs. devilish nondeterminism. In this paper, we propose a simple extension to the standard situation calculus that accommodates nondeterministic actions and preserves Reiter’s solution to the frame problem and answering projection queries through regression. We also provide a formalization of FOND planning and show how ConGolog high-level program execution in nondeterministic domains can be defined.

scholarly journals An Adaptive Parallel Method for Indexing Transportation Moving Objects

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Kun-lun Chen ◽  
Chuan-wen Li ◽  
Guang Lu ◽  
Jia-quan Li ◽  
Tong Zhang
Keyword(s):  
Traffic Control ◽  
Moving Objects ◽  
Skewed Distribution ◽  
Grid Data ◽  
Quad Tree ◽  
Indexing Method ◽  
Object Databases ◽  
High Level ◽  
Data Updating ◽  
Moving Object Databases

Transportation cyber-physical systems are constrained by spatiality and real-time because of their high level of heterogeneity. Therefore, applications like traffic control generally manage moving objects in a single-machine multithreaded manner, whereas suffering from frequent locking operations. To address this problem and improve the throughput of moving object databases, we propose a GPU-accelerated indexing method, based on a grid data structure, combined with quad-trees. We count object movements and decide whether a particular node should be split or be merged on the GPU. In this case, bottlenecked nodes can be translated to quad-tree without interfering with the CPU. Hence, waiting time of other threads caused by locking operations raised by object data updating can be reduced. The method is simple while more adaptive to scenarios where the distribution of moving objects is skewed. It also avoids shortcomings of existing methods with performance bottleneck on the hot area or spending plenty of calculation resources on structure balancing. Experiments suggest that our method shows higher throughput and lower response time than the existing indexing methods. The advantage is even more significant under the skewed distribution of moving objects.

Abstract T P123: Impairment in Cognitively Demanding Driving Situations after Acute Mild Ischemic Stroke

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Megan A Hird ◽  
Kristin A Vesely ◽  
Leah E Christie ◽  
Melissa A Alves ◽  
Jitphapa Pongmoragot ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Phase ◽  
Driving Simulator ◽  
Longitudinal Research ◽  
Driving Performance ◽  
Stroke Patients ◽  
Post Stroke ◽  
Mild Stroke ◽  
Left Turns ◽  
Right Turns

Introduction: Guidelines established by prominent governing bodies recommend that patients should wait a minimum of one month before resuming driving after stroke; however, these guidelines are not based on empirical evidence. Furthermore, many patients report resuming driving within the one-month period post-stroke. The aim of this study was to investigate the driving performance of mild stroke patients within the acute phase of injury. It was hypothesized that patients with acute stroke would exhibit more errors in general (e.g. collisions, speed exceedances, centre line crossings) and during cognitively demanding aspects of driving (i.e. left turns with traffic), but not routine aspects of driving (i.e. straight driving and right turns). Methods: The current study used driving simulator technology (STISIM) to compare the driving performance of 10 patients with acute mild ischemic stroke (NIHSS<7, within 7 days post-stroke) to that of 10 healthy, age- and education-matched controls. Patients and controls completed several driving tasks that increased in complexity, from routine right and left turns to cognitively demanding left turns with traffic, where most accidents occur, and a bus following task, which requires a high degree of sustained attention. Results: On average, stroke patients committed over twice as many errors as controls (12.4 vs 6.0, p< 0.01). Although there was no difference between patients and controls in the number of errors committed during routine right and left turns, patients committed more errors during left turns with traffic (2.4 vs 1.3, p<0.05) and a bus following task (8.2 vs 2.1, p<0.05). Conclusions: Patients with acute mild ischemic stroke may be able to maintain driving performance during basic tasks (e.g. straight driving, right turns) and deficits may become apparent during cognitively complex tasks (e.g. left turns with traffic and bus following). The results highlight the importance of healthcare professionals providing driving advice to their patients post-stroke, particularly in the acute phase of injury. Future longitudinal research is required to determine when patients with mild stroke can safely resume driving.

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