scholarly journals Parallelizing Plan Recognition

AI Magazine ◽  
2015 ◽  
Vol 36 (2) ◽  
pp. 22-32
Author(s):  
Christopher W. Geib ◽  
Christopher E. Swetenham
Keyword(s):  
Plan Recognition ◽  
Breadth First Search ◽  
Problem Domain ◽  
Intent Recognition ◽  
Recognition Algorithms ◽  
Linear Speedup ◽  
Computing Platforms ◽  
Multicore Computers ◽  
Modern Computing

Modern multicore computers provide an opportunity to parallelize plan recognition algorithms to decrease runtime. Viewing plan recognition as parsing based on a complete breadth first search, makes ELEXIR (engine for lexicalized intent recognition) (Geib 2009; Geib and Goldman 2011) particularly suited for parallelization. This article documents the extension of ELEXIR to utilize such modern computing platforms. We will discuss multiple possible algorithms for distributing work between parallel threads and the associated performance wins. We will show, that the best of these algorithms provides close to linear speedup (up to a maximum number of processors), and that features of the problem domain have an impact on the achieved speedup.

INTENT RECOGNITION IN MULTI-AGENT SYSTEMS: COW HERDING

2014 ◽  
Vol 28 (05) ◽  
pp. 1459006
Author(s):  
NAJLA AHMAD ◽  
ARVIN AGAH
Keyword(s):  
Conceptual Framework ◽  
Recognition System ◽  
Plan Recognition ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent System ◽  
Intent Recognition ◽  
Domain Specific ◽  
Novel Approach ◽  
Multi Agent

In a multi-agent system, an agent may utilize its idle time to assist other agents in the system. Intent recognition is proposed to accomplish this with minimal communication. An agent performing recognition observes the tasks other agents are performing and, unlike the much studied field of plan recognition, the overall intent of an agent is recognized instead of a specific plan. The observing agent may use capabilities that it has not observed. A conceptual framework is proposed for intent recognition systems. An implementation of the conceptual framework is tested and evaluated. We hypothesize that using intent recognition in a multi-agent system increases utility (where utility is domain specific) and decreases the amount of communication. We test our hypotheses using the domain of Cow Herding, where agents attempt to herd cow agents into team corrals. A set of metrics, including task time and number of communications, is used to compare the performance of plan recognition and intent recognition. In our results, we find that intent recognition agents communicate fewer times than plan recognition agents. In addition, unlike plan recognition, when agents use the novel approach of intent recognition, they select unobserved actions to perform. Intent recognition agents were also able to outperform plan recognition agents by consistently scoring more points in the Cow Herding domain. This research shows that under certain conditions, an intent recognition system is more efficient than a plan recognition system. The advantage of intent recognition over plan recognition becomes more apparent in complex domains.

SnBversion 2.2: an example of crystallographic multiprocessing

2002 ◽  
Vol 35 (3) ◽  
pp. 374-376 ◽  
Author(s):  
Jason Rappleye ◽  
Martins Innus ◽  
Charles M. Weeks ◽  
Russ Miller
Keyword(s):  
Shared Memory ◽  
Distributed Memory ◽  
Computing Time ◽  
Direct Methods ◽  
Fixed Number ◽  
Linear Speedup ◽  
Beowulf Clusters ◽  
Shared Memory Multiprocessor ◽  
Computing Platforms ◽  
Time Required

The computer programSnBimplements a direct-methods algorithm, known asShake-and-Bake, which optimizes trial structures consisting of randomly positioned atoms. Although largeShake-and-Bakeapplications require significant amounts of computing time, the algorithm can be easily implemented in parallel in order to decrease the real time required to achieve a solution. By using a master–worker model,SnBversion 2.2 is amenable to all of the prevalent modern parallel-computing platforms, including (i) shared-memory multiprocessor machines, such as the SGI Origin2000, (ii) distributed-memory multiprocessor machines, such as the IBM SP, and (iii) collections of workstations, including Beowulf clusters. A linear speedup in the processing of a fixed number of trial structures can be obtained on each of these platforms.

scholarly journals Parallel Scheduling of Grid Jobs on Quadcore Systems using Grouping Methods

2021 ◽  
pp. 21-34
Author(s):  
Goodhead T. Abraham ◽  
Evans F. Osaisai ◽  
Nicholas S. Dienagha
Keyword(s):  
Scheduling Algorithm ◽  
Group Method ◽  
Sequential Method ◽  
System A ◽  
Processing Power ◽  
Significant Performance ◽  
Computing Platforms ◽  
Machine Groups ◽  
Multicore Computers ◽  
Single Processor

As Grid computing continues to make inroads into different spheres of our lives and multicore computers become ubiquitous, the need to leverage the gains of multicore computers for the scheduling of Grid jobs becomes a necessity. Most Grid schedulers remain sequential in nature and are inadequate in meeting up with the growing data and processing need of the Grid. Also, the leakage of Moore’s dividend continues as most computing platforms still depend on the underlying hardware for increased performance. Leveraging the Grid for the data challenge of the future requires a shift away from the traditional sequential method. This work extends the work of [1] on a quadcore system. A random method was used to group machines and the total processing power of machines in each group was computed, a size proportional to speed method is then used to estimates the size of jobs for allocation to machine groups. The MinMin scheduling algorithm was implemented within the groups to schedule a range of jobs while varying the number of groups and threads. The experiment was executed on a single processor system and on a quadcore system. Significant improvement was achieved using the group method on the quadcore system compared to the ordinary MinMin on the quadcore. We also find significant performance improvement with increasing groups. Thirdly, we find that the MinMin algorithm also gained marginally from the quadcore system meaning that it is also scalable.

Ensemble-Based Software Engineering for Modern Computing Platforms

2020 ◽  
Vol 45 (1) ◽  
pp. 28-30
Author(s):  
Antonio Brogi ◽  
Antonio Bucchiarone ◽  
Rafael Capilla ◽  
Pooyan Jamshidi ◽  
Maurizio Leotta ◽  
...  
Keyword(s):  
Software Engineering ◽  
Computing Platforms ◽  
Modern Computing

scholarly journals Comparing Plan Recognition Algorithms Through Standard Plan Libraries

2022 ◽  
Vol 4 ◽  
Author(s):  
Reuth Mirsky ◽  
Ran Galun ◽  
Kobi Gal ◽  
Gal Kaminka
Keyword(s):  
User Interfaces ◽  
Cyber Security ◽  
Evaluation Criteria ◽  
Computation Time ◽  
Plan Recognition ◽  
Discrete Space ◽  
Standard Representation ◽  
Recognition Process ◽  
Common Representation ◽  
Recognition Algorithms

Plan recognition deals with reasoning about the goals and execution process of an actor, given observations of its actions. It is one of the fundamental problems of AI, applicable to many domains, from user interfaces to cyber-security. Despite the prevalence of these approaches, they lack a standard representation, and have not been compared using a common testbed. This paper provides a first step towards bridging this gap by providing a standard plan library representation that can be used by hierarchical, discrete-space plan recognition and evaluation criteria to consider when comparing plan recognition algorithms. This representation is comprehensive enough to describe a variety of known plan recognition problems and can be easily used by existing algorithms in this class. We use this common representation to thoroughly compare two known approaches, represented by two algorithms, SBR and Probabilistic Hostile Agent Task Tracker (PHATT). We provide meaningful insights about the differences and abilities of these algorithms, and evaluate these insights both theoretically and empirically. We show a tradeoff between expressiveness and efficiency: SBR is usually superior to PHATT in terms of computation time and space, but at the expense of functionality and representational compactness. We also show how different properties of the plan library affect the complexity of the recognition process, regardless of the concrete algorithm used. Lastly, we show how these insights can be used to form a new algorithm that outperforms existing approaches both in terms of expressiveness and efficiency.

scholarly journals On clustering DAGs for task-hungry computing platforms

2011 ◽  
Vol 1 (1) ◽  
Author(s):  
Gennaro Cordasco ◽  
Arnold Rosenberg ◽  
Mark Sims
Keyword(s):  
Current Paper ◽  
Computing Platforms ◽  
Computational Resources ◽  
Task Clustering ◽  
Modern Computing

AbstractMany modern computing platforms are “task-hungry”: their performance is enhanced by always having as many tasks available for execution as possible. IC-scheduling, a master-worker framework for executing static computations that have intertask dependencies (modeled as dags), was developed with precisely the goal of rendering a computation-dag’s tasks eligible for execution at the maximum possible rate. The current paper addresses the problem of enhancing IC-scheduling so that it can accommodate the varying computational resources of different workers, by clustering a computation-dag’s tasks, while still producing eligible (now, clustered) tasks at the maximum possible rate. The task-clustering strategies presented exploit the structure of the computation being performed, ranging from a strategy that works for any dag, to ones that build increasingly on the explicit structure of the dagbeing scheduled.

scholarly journals High-performance computing platforms: current status and development trends

2021 ◽  
pp. 135-177
Author(s):  
А.С. Антонов ◽  
И.В. Афанасьев ◽  
Вл.В. Воеводин
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Current Status ◽  
Development Trends ◽  
Current State ◽  
The World ◽  
Points Of View ◽  
Computing Platforms ◽  
Performance Computing ◽  
Modern Computing

В данной статье представлен обзор современного состояния суперкомпьютерной техники. Обзор сделан с разных точек зрения — начиная от особенностей построения современных вычислительных устройств до особенностей архитектуры больших суперкомпьютерных комплексов. В данный обзор вошли описания самых мощных суперкомпьютеров мира и России по состоянию на начало 2021 г., а также некоторых менее мощных систем, интересных с других точек зрения. Также делается акцент на тенденциях развития суперкомпьютерной отрасли и описываются наиболее известные проекты построения будущих экзафлопсных суперкомпьютеров. This paper provides an overview of the current state of supercomputer technology. The review is done from different points of view — from the construction features of modern computing devices to the features of the architecture of large supercomputer complexes. This review includes descriptions of the most powerful supercomputers in the world and Russia since the early of 2021 as well as some less powerful systems that are interesting from other points of view. It also focuses on the development trends of the supercomputer industry and describes the most famous projects for building future exascale supercomputers.

scholarly journals Plan and Intent Recognition in a Multi-agent System for Collective Box Pushing

2014 ◽  
Vol 23 (1) ◽  
pp. 95-108 ◽  
Author(s):  
Najla Ahmad ◽  
Arvin Agah
Keyword(s):  
Conceptual Framework ◽  
Research Question ◽  
Recognition System ◽  
Plan Recognition ◽  
Multi Agent System ◽  
Agent System ◽  
Intent Recognition ◽  
Multi Agent ◽  
Task Time ◽  
Recognition Systems

AbstractIn a distributed multi-agent system, an idle agent may be available to assist other agents in the system. An agent architecture called intent recognition is proposed in this article to accomplish this with minimal communication. To assist other agents in the system, an agent performing recognition observes the tasks other agents are performing. Unlike the much-studied field of plan recognition, the overall intent of an agent is recognized instead of a specific plan. The observing agent may use capabilities that it has not observed. In this study, the key research question is: What are intent-recognition systems and how can these be used to have agents autonomously assist each other effectively and efficiently? A conceptual framework is proposed to address this question. An implementation of the conceptual framework is tested and evaluated. A set of metrics, including task time and number of communications, is used to compare the performance of plan recognition and intent recognition. This research shows that under certain conditions, an intent-recognition system is more efficient than a plan recognition system.

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