scholarly journals Data-Driven Analysis for Understanding Team Sports Behaviors

2021 ◽  
Vol 33 (3) ◽  
pp. 505-514
Author(s):  
Keisuke Fujii ◽  
◽  
◽  
Keyword(s):  
Real World ◽  
Team Sports ◽  
Data Driven ◽  
Mathematical Structures ◽  
The Real ◽  
Practical Applications ◽  
Body Dynamics ◽  
Quantitative Understanding ◽  
Multi Agent ◽  
Controlling Behaviors

Understanding the principles of real-world biological multi-agent behaviors is a current challenge in various scientific and engineering fields. The rules regarding the real-world biological multi-agent behaviors such as those in team sports are often largely unknown due to their inherently higher-order interactions, cognition, and body dynamics. Estimation of the rules from data, i.e., via data-driven approaches such as machine learning, provides an effective way to analyze such behaviors. Although most data-driven models have non-linear structures and high predictive performances, it is sometimes hard to interpret them. This survey focuses on data-driven analysis for quantitative understanding of behaviors in invasion team sports such as basketball and football, and introduces two main approaches for understanding such multi-agent behaviors: (1) extracting easily interpretable features or rules from data and (2) generating and controlling behaviors in visually-understandable ways. The first approach involves the visualization of learned representations and the extraction of mathematical structures behind the behaviors. The second approach can be used to test hypotheses by simulating and controlling future and counterfactual behaviors. Lastly, the potential practical applications of extracted rules, features, and generated behaviors are discussed. These approaches can contribute to a better understanding of multi-agent behaviors in the real world.

scholarly journals Agent-Based Distributed Planning and Coordination for Resilient Airport Surface Movement Operations

Aerospace ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 48
Author(s):  
Konstantine Fines ◽  
Alexei Sharpanskykh ◽  
Matthieu Vert
Keyword(s):  
Real World ◽  
Coordination Mechanisms ◽  
Multi Agent System ◽  
Distributed Planning ◽  
Agent Based ◽  
Surface Movement ◽  
The Real ◽  
Complex Processes ◽  
Multi Agent ◽  
Operational Data

Airport surface movement operations are complex processes with many types of adverse events which require resilient, safe, and efficient responses. One regularly occurring adverse event is that of runway reconfiguration. Agent-based distributed planning and coordination has shown promising results in controlling operations in complex systems, especially during disturbances. In contrast to the centralised approaches, distributed planning is performed by several agents, which coordinate plans with each other. This research evaluates the contribution of agent-based distributed planning and coordination to the resilience of airport surface movement operations when runway reconfigurations occur. An autonomous Multi-Agent System (MAS) model was created based on the layout and airport surface movement operations of Schiphol Airport in the Netherlands. Within the MAS model, three distributed planning and coordination mechanisms were incorporated, based on the Conflict-Based Search (CBS) Multi-Agent Path Finding (MAPF) algorithm and adaptive highways. MAS simulations were run based on eight days of real-world operational data from Schiphol Airport and the results of the autonomous MAS simulations were compared to the performance of the real-world human operated system. The MAS results show that the distributed planning and coordination mechanisms were effective in contributing to the resilient behaviour of the airport surface movement operations, closely following the real-world behaviour, and sometimes even surpassing it. In particular, the mechanisms were found to contribute to more resilient behaviour than the real-world when considering the taxi time after runway reconfiguration events. Finally, the highway included distributed planning and coordination mechanisms contributed to the most resilient behaviour of the airport surface movement operations.

scholarly journals Multi-agent systems for the real world

2010 ◽  
Author(s):  
Rajiv T. Maheswaran ◽  
Craig M. Rogers ◽  
Romeo Sanchez ◽  
Pedro Szekely ◽  
Robert Neches
Keyword(s):  
Real World ◽  
Multi Agent Systems ◽  
The Real ◽  
Agent Systems ◽  
Multi Agent

scholarly journals Estructuras y representaciones

1990 ◽  
Vol 22 (64) ◽  
pp. 3-22
Author(s):  
Adolfo García de la Sienra
Keyword(s):  
Real World ◽  
Representation Theorem ◽  
Mathematical Structures ◽  
The Real ◽  
Conceptual Apparatus ◽  
Philosophical Basis ◽  
Aristotelian Metaphysics

The aim of the present paper is to set a philosophical basis in order to discuss the type of representation that holds between mathematical structures and those aspects of the real world which they represent. It is maintained that an actualized version of Aristotelian metaphysics is suited for this purpose. The connection between the abstract, rigid concepts of mathematics, and the concepts of metaphysics is attempted through the concept of a fundamental measurement. The existence and degree of uniqueness of a fundamental measurement is established as a representation theorem asserting the existence of a homomorphism from what I call an ontological structure into a numerical one. An ontological structure contains as elements real beings, and its relations represent —in a sense made precise thereof— real relations among these beings. The role of metaphysics in the establishment of a representation theorem is to provide the conceptual apparatus required to discuss and formulate the ontological axioms required to derive the theorem. The paper contains a very complete example of a fundamental measurement in the sense described, namely, the measurement of the height of a physical parallelepiped and that of its potential parts.

scholarly journals Coffee With a Hint of Data: Towards Using Data-Driven Approaches in Personalised Long-Term Interactions

2021 ◽  
Vol 8 ◽  
Author(s):  
Bahar Irfan ◽  
Mehdi Hellou ◽  
Tony Belpaeme
Keyword(s):  
Real World ◽  
Poor Performance ◽  
User Preferences ◽  
Human Robot Interaction ◽  
Data Driven ◽  
Rule Based ◽  
The Real ◽  
Related Data ◽  
Memory Network

While earlier research in human-robot interaction pre-dominantly uses rule-based architectures for natural language interaction, these approaches are not flexible enough for long-term interactions in the real world due to the large variation in user utterances. In contrast, data-driven approaches map the user input to the agent output directly, hence, provide more flexibility with these variations without requiring any set of rules. However, data-driven approaches are generally applied to single dialogue exchanges with a user and do not build up a memory over long-term conversation with different users, whereas long-term interactions require remembering users and their preferences incrementally and continuously and recalling previous interactions with users to adapt and personalise the interactions, known as the lifelong learning problem. In addition, it is desirable to learn user preferences from a few samples of interactions (i.e., few-shot learning). These are known to be challenging problems in machine learning, while they are trivial for rule-based approaches, creating a trade-off between flexibility and robustness. Correspondingly, in this work, we present the text-based Barista Datasets generated to evaluate the potential of data-driven approaches in generic and personalised long-term human-robot interactions with simulated real-world problems, such as recognition errors, incorrect recalls and changes to the user preferences. Based on these datasets, we explore the performance and the underlying inaccuracies of the state-of-the-art data-driven dialogue models that are strong baselines in other domains of personalisation in single interactions, namely Supervised Embeddings, Sequence-to-Sequence, End-to-End Memory Network, Key-Value Memory Network, and Generative Profile Memory Network. The experiments show that while data-driven approaches are suitable for generic task-oriented dialogue and real-time interactions, no model performs sufficiently well to be deployed in personalised long-term interactions in the real world, because of their inability to learn and use new identities, and their poor performance in recalling user-related data.

scholarly journals Bringing Multi-agent Path Finding Closer to Reality

2018 ◽  
Author(s):  
Jiří Švancara
Keyword(s):  
Real World ◽  
Path Finding ◽  
Multiple Agents ◽  
New Agents ◽  
The Real ◽  
Classical Definition ◽  
Multi Agent ◽  
Definition Of

Multi-agent path finding is the problem of navigating multiple agents from their current locations to their goal locations in such a way that there are no collisions between the agents. The classical definition of the problem assumes that the set of agents is unchangeable, and that the distances in the graph are homogeneous. We propose to add to the problem specification a set of new attributes to bring it closer to the real world. These attributes include varying distances, number of agents that can occupy an edge or node, and dynamic appearance of new agents.

scholarly journals Costruire scenari per il futuro

2017 ◽  
Vol 8 (1) ◽  
pp. 117-146
Author(s):  
Silvano Tagliagambe
Keyword(s):  
Decision Making ◽  
Real World ◽  
Social Economic ◽  
A Priori ◽  
Interdisciplinary Approach ◽  
Data Driven ◽  
The Real ◽  
Unified Theories ◽  
The World ◽  
Project Data

In 2008 Chris Anderson wrote a provocative piece titled The End of Theory. The idea being that we no longer need to abstract and hypothesis; we simply need to let machines lead us to the patterns, trends, and relationships in social, economic, political, and environmental relationships. According to Anderson, the new availability of huge amounts of data offers a whole new way of understanding the world. Correlation supersedes causation, and science can advance even without coherent models and unified theories. But numbers, contrary to Anderson’s assertion, do not, in fact, speak for themselves. From the neuroscience’s standpoint, every choice we make is a reflection of an, often unstated, set of assumptions and hypotheses about what we want and expect from the data: no assertion, no prediction, no decision making is possible without an a priori opinion, without a project. Data-driven science essentially refers to the application of mathematics and technology on data to extract insights for problems, which are very clearly defined. In the real world, however, not all problems are such. To help solve them, one needs to understand and appreciate the context. The problem of landscape becomes, for this reason, critical and decisive. It requires an interdisciplinary approach consisting of several different competencies and skills.

Mechanisms to Restrict Exploitation and Improve Societal Performance in Multi-Agent Systems

2011 ◽  
pp. 182-194
Author(s):  
Sharmila Savarimuthu ◽  
Martin Purvis ◽  
Maryam Purvis ◽  
Mariusz Nowostawski
Keyword(s):  
Resource Utilization ◽  
Real World ◽  
Experimental Results ◽  
Multi Agent Systems ◽  
The Real ◽  
Agent Systems ◽  
Multi Agent ◽  
Overall Performance

Societies are made of different kinds of agents, some cooperative and uncooperative. Uncooperative agents tend to reduce the overall performance of the society, due to exploitation practices. In the real world, it is not possible to decimate all the uncooperative agents; thus the objective of this research is to design and implement mechanisms that will improve the overall benefit of the society without excluding uncooperative agents. The mechanisms that we have designed include referrals and resource restrictions. A referral scheme is used to identify and distinguish noncooperators and cooperators. Resource restriction mechanisms are used to restrict noncooperators from selfish resource utilization. Experimental results are presented describing how these mechanisms operate.

Current Issues: Evidence-Based Practice for the Real World: Thoughts on Clinical Education in Communication Disorders

2010 ◽  
Vol 20 (3) ◽  
pp. 100-105 ◽  
Author(s):  
Anne K. Bothe
Keyword(s):  
Clinical Education ◽  
Real World ◽  
Evidence Based Practice ◽  
Communication Disorders ◽  
University Faculty ◽  
Evidence Based ◽  
Speech Language Pathology ◽  
Basic Principles ◽  
The Real ◽  
Language Pathology

This article presents some streamlined and intentionally oversimplified ideas about educating future communication disorders professionals to use some of the most basic principles of evidence-based practice. Working from a popular five-step approach, modifications are suggested that may make the ideas more accessible, and therefore more useful, for university faculty, other supervisors, and future professionals in speech-language pathology, audiology, and related fields.

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