scholarly journals Deception in Finitely Repeated Security Games

2019 ◽  
Vol 33 ◽  
pp. 2133-2140 ◽  
Author(s):  
Thanh H. Nguyen ◽  
Yongzhao Wang ◽  
Arunesh Sinha ◽  
Michael P. Wellman
Keyword(s):  
Learning Process ◽  
Optimal Strategies ◽  
Computational Experiments ◽  
Repeated Interaction ◽  
Security Games ◽  
Attack Data ◽  
Type Information ◽  
Detailed Computation ◽  
Over Time

Allocating resources to defend targets from attack is often complicated by uncertainty about the attacker’s capabilities, objectives, or other underlying characteristics. In a repeated interaction setting, the defender can collect attack data over time to reduce this uncertainty and learn an effective defense. However, a clever attacker can manipulate the attack data to mislead the defender, influencing the learning process toward its own benefit. We investigate strategic deception on the part of an attacker with private type information, who interacts repeatedly with a defender. We present a detailed computation and analysis of both players’ optimal strategies given the attacker may play deceptively. Computational experiments illuminate conditions conducive to strategic deception, and quantify benefits to the attacker. By taking into account the attacker’s deception capacity, the defender can significantly mitigate loss from misleading attack actions.

First encounter effects in testing of highly automated vehicles during two experimental occasions – The need for recurrent testing

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jonas Andersson ◽  
Azra Habibovic ◽  
Daban Rizgary
Keyword(s):  
Driver Behavior ◽  
Added Value ◽  
Future Research ◽  
Gaze Behavior ◽  
Repeated Testing ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Perceived Safety ◽  
Repeated Interaction ◽  
Over Time

Abstract To explore driver behavior in highly automated vehicles (HAVs), independent researchers are mainly conducting short experiments. This limits the ability to explore drivers’ behavioral changes over time, which is crucial when research has the intention to reveal human behavior beyond the first-time use. The current paper shows the methodological importance of repeated testing in experience and behavior related studies of HAVs. The study combined quantitative and qualitative data to capture effects of repeated interaction between drivers and HAVs. Each driver ( n = 8 n=8 ) participated in the experiment on two different occasions (∼90 minutes) with one-week interval. On both occasions, the drivers traveled approximately 40 km on a rural road at AstaZero proving grounds in Sweden and encountered various traffic situations. The participants could use automated driving (SAE level 4) or choose to drive manually. Examples of data collected include gaze behavior, perceived safety, as well as interviews and questionnaires capturing general impressions, trust and acceptance. The analysis shows that habituation effects were attenuated over time. The drivers went from being exhilarated on the first occasion, to a more neutral behavior on the second occasion. Furthermore, there were smaller variations in drivers’ self-assessed perceived safety on the second occasion, and drivers were faster to engage in non-driving related activities and become relaxed (e. g., they spent more time glancing off road and could focus more on non-driving related activities such as reading). These findings suggest that exposing drivers to HAVs on two (or more) successive occasions may provide more informative and realistic insights into driver behavior and experience as compared to only one occasion. Repeating an experiment on several occasions is of course a balance between the cost and added value, and future research should investigate in more detail which studies need to be repeated on several occasions and to what extent.

scholarly journals Learning Optimal Strategies to Commit To

2019 ◽  
Vol 33 ◽  
pp. 2149-2156 ◽  
Author(s):  
Binghui Peng ◽  
Weiran Shen ◽  
Pingzhong Tang ◽  
Song Zuo
Keyword(s):  
National Security ◽  
Prior Knowledge ◽  
Optimal Strategies ◽  
Payoff Matrix ◽  
Stackelberg Games ◽  
Full Information ◽  
The Past ◽  
Security Games ◽  
Payoff Matrices ◽  
Novel Algorithms

Over the past decades, various theories and algorithms have been developed under the framework of Stackelberg games and part of these innovations have been fielded under the scenarios of national security defenses and wildlife protections. However, one of the remaining difficulties in the literature is that most of theoretical works assume full information of the payoff matrices, while in applications, the leader often has no prior knowledge about the follower’s payoff matrix, but may gain information about the follower’s utility function through repeated interactions. In this paper, we study the problem of learning the optimal leader strategy in Stackelberg (security) games and develop novel algorithms as well as new hardness results.

Wilding liability in education: Introducing the concept of wide risk as counterpoint to narrow-risk-driven educative practice

2020 ◽  
pp. 147821032097809
Author(s):  
Chris Beeman
Keyword(s):  
Mental Health ◽  
Risk Management ◽  
Public Schools ◽  
Public Education ◽  
Learning Process ◽  
Natural World ◽  
Ethical Responsibility ◽  
Learning Outdoors ◽  
Reduce Risk ◽  
Over Time

Consideration of risk and liability in outdoor educative practice has normally been limited to the narrow risks, usually to physical health, of incidents that can cause a particular injury. In this view of risk management, the more readily controlled the circumstance, the less likelihood of risk and consequent liability. Thus, to reduce risk, learning in the natural world is often avoided because it occurs in far more complex and less controllable contexts than human-created ones. However, wider and more grave risks to physical, emotional and mental health that may accrue through a life that is lived in separation from the natural world are not often considered or evaluated. In part, this may be because these kinds of risks are less immediately evident, and liability for negative outcomes may be more difficult to measure. Thus, there is less incentive to consider them. However, delayed outcomes are still outcomes. To consider easily discerned narrow risk alone, while ignoring more complex and longer-term wide risk, is no excuse for avoiding the ethical responsibility that public education carries to provide both the safest and most fecund context for learning. This paper introduces the concept of wide risk as a counterpoint to the narrow risk calculations now performed, and argues that in incorporating an understanding of wide risk in educative practice, at least two results are likely. The first is that learning outdoors will frequently be discovered to be a less risky alternative, if a broad range of outcomes over time are considered. The second is that the value of embracing risk in all aspects of learning ought to become a part of the learning process, and part of what is taught in public schools.

scholarly journals InterSpot: Interactive Spammer Detection in Social Media

2019 ◽  
Author(s):  
Kaize Ding ◽  
Jundong Li ◽  
Shivam Dhar ◽  
Shreyash Devan ◽  
Huan Liu
Keyword(s):  
Social Media ◽  
Learning Process ◽  
Interactive Learning ◽  
Detection System ◽  
Feedback Information ◽  
Detection Model ◽  
Empirical Success ◽  
Detection Strategy ◽  
Current Detection ◽  
Over Time

Spammer detection in social media has recently received increasing attention due to the rocketing growth of user-generated data. Despite the empirical success of existing systems, spammers may continuously evolve over time to impersonate normal users while new types of spammers may also emerge to combat with the current detection system, leading to the fact that a built system will gradually lose its efficacy in spotting spammers. To address this issue, grounded on the contextual bandit model, we present a novel system for conducting interactive spammer detection. We demonstrate our system by showcasing the interactive learning process, which allows the detection model to keep optimizing its detection strategy through incorporating the feedback information from human experts.

scholarly journals Imitative Attacker Deception in Stackelberg Security Games

2019 ◽  
Author(s):  
Thanh Nguyen ◽  
Haifeng Xu
Keyword(s):  
Recent Work ◽  
Side Effect ◽  
Learning Algorithms ◽  
Optimal Algorithms ◽  
Potential Side Effect ◽  
Optimal Defense ◽  
Defense Strategy ◽  
Game Equilibrium ◽  
Security Games ◽  
Attack Data

To address the challenge of uncertainty regarding the attacker’s payoffs, capabilities, and other characteristics, recent work in security games has focused on learning the optimal defense strategy from observed attack data. This raises a natural concern that the strategic attacker may mislead the defender by deceptively reacting to the learning algorithms. This paper focuses on understanding how such attacker deception affects the game equilibrium. We examine a basic deception strategy termed imitative deception, in which the attacker simply pretends to have a different payoff assuming his true payoff is unknown to the defender. We provide a clean characterization about the game equilibrium as well as optimal algorithms to compute the equilibrium. Our experiments illustrate significant defender loss due to imitative attacker deception, suggesting the potential side effect of learning from the attacker.

PRIOR KNOWLEDGE VERSUS CONSTRUCTED KNOWLEDGE: WHAT IMPACT ON LEARNING?

2008 ◽  
Vol 11 (02) ◽  
pp. 289-302 ◽  
Author(s):  
WIDAD GUECHTOULI
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Prior Knowledge ◽  
Learning Process ◽  
Agent Based Model ◽  
Agent Based ◽  
Congestion Effect ◽  
Over Time

The aim of this paper is to model the process of learning within a social network and compare the levels of learning in two different situations: one where individuals know others' competencies as given data and interact on this basis; and one where individuals know nothing about others' competencies but rather build this knowledge over time, according to their past interactions. For this purpose, we build an agent-based model, and model these two scenarios of simulations. Results are partly studied using network analysis, and they show that in the second type of simulations agents are able to identify the most competent agents in the network and increase their competencies. Results also show that learning is easier when there is no prior knowledge of others' competencies. Otherwise, agents deal with a congestion effect that slows down the learning process.

Crowd Learning

Crowdsourcing ◽  
2019 ◽  
pp. 1587-1605
Author(s):  
David Elijah Kalisz
Keyword(s):  
Learning Process ◽  
Formal Education ◽  
Student Experience ◽  
Education Management ◽  
Knowledge Processes ◽  
Research In Education ◽  
Future Education ◽  
Future Learning ◽  
Learning Concept ◽  
Over Time

Understanding sources of learning has become a major area of research in Education Management. Building on the assumptions that crowd learning is distributed across societies and education institutions and that it creates an innovative perspective for education for next-generation over the time, this article examines the link between formal education and innovative crowd-created knowledge. The article concludes by examining implications of crowd learning concept for actual and future education management systems. This paper explores how the crowd learns and remembers over time in the context, and how more realistic assumptions of student experience may be used in building crowd knowledge processes. The aim of the paper is to determine the assessment of crowd learning, its history, concepts and its influence on future learning process, including the changing instructor's role.

Crowd Learning

2016 ◽  
pp. 55-74
Author(s):  
David Elijah Kalisz
Keyword(s):  
Learning Process ◽  
Formal Education ◽  
Student Experience ◽  
Education Management ◽  
Knowledge Processes ◽  
Research In Education ◽  
Future Education ◽  
Future Learning ◽  
Learning Concept ◽  
Over Time

Understanding sources of learning has become a major area of research in Education Management. Building on the assumptions that crowd learning is distributed across societies and education institutions and that it creates an innovative perspective for education for next-generation over the time, this article examines the link between formal education and innovative crowd-created knowledge. The article concludes by examining implications of crowd learning concept for actual and future education management systems. This paper explores how the crowd learns and remembers over time in the context, and how more realistic assumptions of student experience may be used in building crowd knowledge processes. The aim of the paper is to determine the assessment of crowd learning, its history, concepts and its influence on future learning process, including the changing instructor's role.

Understanding the pedagogical practice in futsal teaching and learning process based on the TGfU approach

2022 ◽  
pp. 174795412110707
Author(s):  
Guy Ginciene ◽  
Camila Amato ◽  
Eduardo Rodrigues de Oliveira ◽  
Ivan Oliveira dos Santos ◽  
Eduardo Dell Osbel ◽  
...  
Keyword(s):  
Learning Process ◽  
Research Group ◽  
Teaching And Learning ◽  
Diagnostic Assessment ◽  
Pedagogical Practices ◽  
Pedagogical Practice ◽  
University Professors ◽  
Assistant Coach ◽  
Over Time ◽  
Made In

The aim of the current study is to understand the pedagogical practice of coaches of youth futsal players based on the TGfU approach. Action Research (AR) approach, developed according to the planning, teaching, observing and reflecting spiral steps, was applied for the teaching of futsal for 12 children aged 9–11 years. An eight-member research group (five student coaches, one research coach and two university professors) was subjected to the same practice. Information was collected from class planning meetings, diagnostic assessment and field diaries of participant and non-participant observers. Based on results, changes in the pedagogical practice were aimed at helping players understanding and using actions to support futsal improvements. AR application also highlighted the emerging understanding about student-coach practices, the influence of coaches who adopt reflective practices or not; and the relevance of using critical player observation at the time of reflecting on how to develop supportive pedagogical practices. Results have shown that the pedagogical practice of coaches has changed over time. The researcher-coach practice and the assistant coach (student-coach 1) was influenced by the perceptions of student-coaches and reflections made in meetings.

Thinking Through Exposure

2019 ◽  
pp. 11-30
Author(s):  
Jasper A. J. Smits ◽  
Mark B. Powers ◽  
Michael W. Otto
Keyword(s):  
Active Learning ◽  
Learning Process ◽  
Exposure Therapy ◽  
Outcome Expectancies ◽  
False Alarms ◽  
Potential Threat ◽  
New Learning ◽  
The Difference ◽  
Over Time ◽  
Negative Outcome

Chapter 2 introduces a model of fears in terms of a network of learned associations among interconnected nodes. When these memories are cued, they can elicit expectancies for potential threat outcomes. Exposure therapy is used to alter these danger expectancies through new learning through confronting feared cues. This is an active learning process in which patients learn unconditional safety in response to their fear cues across diverse contexts. Over time, patients learn the difference between danger and fear (true vs. false alarms). To achieve this, it is important to (a) identify negative outcome expectancies to safe but feared cues (false alarms), (b) actively test these expectancies with exposure, (c) conduct postexposure processing of what was (was not) learned, and (d) rehearse this learning between sessions.

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