Artificial intelligence for conflict management

Student Number : 0213053E MSc research report - School of Electrical and Information Engineering - Faculty of Engineering and the Built EnvironmentOne of the risks that have a great impact on society is military con- °ict. Militarised Interstate Dispute (MID) is de¯ned as an outcome of interstate interactions which result in either peace or con°ict. E®ective prediction of the possibility of con°ict between states is a good decision support tool. Neural networks (NNs) have been implemented to predict militarised interstate disputes before Marwala and Lagazio [2004]. Sup- port Vector Machines (SVMs) have proven to be very good prediction techniques in many other real world problems Chen and Odobez [2002]; Pires and Marwala [2004]. In this research we introduce SVMs to predict MID. The results found show that SVM is better in predicting con°ict cases (true positives) without e®ectively reducing the number of correctly classi¯ed peace (true negatives) than NN. A sensitivity analysis for the in°uence of the dyadic (explanatory) variables shows that NN gives more consistent and easy to interpret results than SVM. Further investigation is required with regards to the sensitivity analysis of SVM

Improving conflict support environments with information regarding social relationships

"Advances in artificial intelligence : IBERAMIA 2014 : 14th Ibero-American Conference on AI, Santiago de Chile, Chile, November 24-27, 2014, proceedings", ISBN 978-3-319-12026-3Having knowledge about social interactions as a basis for informed decision support in situations of conflict can be determinant. However, lower attention is given to the social network interpretation process in conflict management approaches. The main objective of the work presented here is to identify how the parties’ social networks correlate to their negotiation performance and how this can be formalized. Therefore, an experiment was set up in which was tried to streamline all the relevant aspects of the interaction between the individual and its environment that occur in a rich sensory environment (where the contextual modalities were monitored). This research explicitly focuses on the idea that an Ambient Intelligence system can create scenarios that augment the possibilities of reaching a positive outcome taking into account the role of contextualized social relationships in various conflict management strategies.This work is part-funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT - Fundac¸ ˜ao para a Ciˆencia e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-028980 (PTDC/EEI-SII/1386/2012) and project PEst-OE/EEI/UI0752/2014

Human-Machine Teamwork: An Exploration of Multi-Agent Systems, Team Cognition, and Collective Intelligence

One of the major ways through which humans overcome complex challenges is teamwork. When humans share knowledge and information, and cooperate and coordinate towards shared goals, they overcome their individual limitations and achieve better solutions to difficult problems. The rise of artificial intelligence provides a unique opportunity to study teamwork between humans and machines, and potentially discover insights about cognition and collaboration that can set the foundation for a world where humans work with, as opposed to against, artificial intelligence to solve problems that neither human or artificial intelligence can solve on its own. To better understand human-machine teamwork, it’s important to understand human-human teamwork (humans working together) and multi-agent systems (how artificial intelligence interacts as an agent that’s part of a group) to identify the characteristics that make humans and machines good teammates. This perspective lets us approach human-machine teamwork from the perspective of the human as well as the perspective of the machine. Thus, to reach a more accurate understanding of how humans and machines can work together, we examine human-machine teamwork through a series of studies. In this dissertation, we conducted 4 studies and developed 2 theoretical models: First, we focused on human-machine cooperation. We paired human participants with reinforcement learning agents to play two game theory scenarios where individual interests and collective interests are in conflict to easily detect cooperation. We show that different reinforcement models exhibit different levels of cooperation, and that humans are more likely to cooperate if they believe they are playing with another human as opposed to a machine. Second, we focused on human-machine coordination. We once again paired humans with machines to create a human-machine team to make them play a game theory scenario that emphasizes convergence towards a mutually beneficial outcome. We also analyzed survey responses from the participants to highlight how many of the principles of human-human teamwork can still occur in human-machine teams even though communication is not possible. Third, we reviewed the collective intelligence literature and the prediction markets literature to develop a model for a prediction market that enables humans and machines to work together to improve predictions. The model supports artificial intelligence operating as a peer in the prediction market as well as a complementary aggregator. Fourth, we reviewed the team cognition and collective intelligence literature to develop a model for teamwork that integrates team cognition, collective intelligence, and artificial intelligence. The model provides a new foundation to think about teamwork beyond the forecasting domain. Next, we used a simulation of emergency response management to test the different teamwork aspects of a variety of human-machine teams compared to human-human and machine-machine teams. Lastly, we ran another study that used a prediction market to examine the impact that having AI operate as a participant rather than an aggregator has on the predictive capacity of the prediction market. Our research will help identify which principles of human teamwork are applicable to human-machine teamwork, the role artificial intelligence can play in enhancing collective intelligence, and the effectiveness of human-machine teamwork compared to single artificial intelligence. In the process, we expect to produce a substantial amount of empirical results that can lay the groundwork for future research of human-machine teamwork

Coordination approaches and systems - part I : a strategic perspective

This is the first part of a two-part paper presenting a fundamental review and summary of research of design coordination and cooperation technologies. The theme of this review is aimed at the research conducted within the decision management aspect of design coordination. The focus is therefore on the strategies involved in making decisions and how these strategies are used to satisfy design requirements. The paper reviews research within collaborative and coordinated design, project and workflow management, and, task and organization models. The research reviewed has attempted to identify fundamental coordination mechanisms from different domains, however it is concluded that domain independent mechanisms need to be augmented with domain specific mechanisms to facilitate coordination. Part II is a review of design coordination from an operational perspective

GHOST: experimenting countermeasures for conflicts in the pilot's activity

An approach for designing countermeasures to cure conflict in aircraft pilots’ activities is presented, both based on Artificial Intelligence and Human Factors concepts. The first step is to track the pilot’s activity, i.e. to reconstruct what he has actually done thanks to the flight parameters and reference models describing the mission and procedures. The second step is to detect conflict in the pilot’s activity, and this is linked to what really matters to the achievement of the mission. The third step is to design accu- rate countermeasures which are likely to do bet- ter than the existing onboard devices. The three steps are presented and supported by experimental results obtained from private and professional pi- lots

Policy Optimization in Automated Point Merge Trajectory Planning: An Artificial Intelligence-based Approach

International audienceAir traffic management is a complex decision making process. Air traffic controllers decision on aircraft trajectory control actions directly lead to the efficiency of traffic flow management. This paper aims to realize an automated routine trajectory management in terminal manoeuvring area with an intelligent decision making agent. An artificial intelligence based approach is applied to adaptively and smartly integrate four types of deconflict actions for resolving conflicts. Especially, the reinforcement learning policy optimization process is discussed in detail. Firstly, application of reinforcement learning in adaptive trajectory planning is presented. The entire problem is adaptively divided into several sub-problems. For each sub-problem, an online policy is applied to guide the simulation and optimization modules to find out the conflict free and less delay solution. The online policy is a scale of weight distribution for choosing desirable actions. It follows the rule of roulette wheel selection with weighted probability. The highest desirable decision variable has the largest share of the roulette wheel, while the lowest desirable decision variable has the smallest share of the roulette wheel. Direct policy optimization algorithm is designed to update the online policy. Finally, experiments are built up for validation of the proposed policy optimization algorithm for the intelligent decision making process. The results in the test environment showed that learning agent with different exploration and exploitation ability will result in different system performance in conflict resolution and delay

Artificial Intelligence, International Competition, and the Balance of Power (May 2018)

World leaders, CEOs, and academics have suggested that a revolution in artificial intelligence is upon us. Are they right, and what will advances in artificial intelligence mean for international competition and the balance of power? This article evaluates how developments in artificial intelligence (AI) — advanced, narrow applications in particular — are poised to influence military power and international politics. It describes how AI more closely resembles “enabling” technologies such as the combustion engine or electricity than a specific weapon. AI’s still-emerging developments make it harder to assess than many technological changes, especially since many of the organizational decisions about the adoption and uses of new technology that generally shape the impact of that technology are in their infancy. The article then explores the possibility that key drivers of AI development in the private sector could cause the rapid diffusion of military applications of AI, limiting first-mover advantages for innovators. Alternatively, given uncertainty about the technological trajectory of AI, it is also possible that military uses of AI will be harder to develop based on private-sector AI technologies than many expect, generating more potential first-mover advantages for existing powers such as China and the United States, as well as larger consequences for relative power if a country fails to adapt. Finally, the article discusses the extent to which U.S. military rhetoric about the importance of AI matches the reality of U.S. investments.LBJ School of Public Affair