Adversarial Search News Today : Breaking News, Live Updates & Top Stories | Vimarsana

Stay updated with breaking news from Adversarial search. Get real-time updates on events, politics, business, and more. Visit us for reliable news and exclusive interviews.

Top News In Adversarial Search Today - Breaking & Trending Today

"Goal-Driven Adversarial Search for Distributed Self-Adaptive Systems" by Saad Sajid Hashmi, Hoa Khanh Dam et al.

Resilience and antifragility are highly desirable properties for systems operating in dynamic, contested environments. Recent work has proposed an approach for achieving antifragility through three new self-? properties, one of which is adversarial self-exploration. In that approach, a single agent is responsible for defending a (distributed) managed system against an adversary. However, a major limitation is that the agent requires full observability of the managed system and its environment, which is not practical in many scenarios-including when a system operates in contested environments. We address this limitation by extending the approach to multi-Agent self-exploration, where agents with partial observability and control of the managed system coordinate with other agents to compute the most resilient responses in the presence of adversaries. We demonstrate the feasibility and scalability of the proposed approach through extensive experimentation. ....

Adversarial Search , Ulti Agent Coordination , Elf Adaptive , Self Exploration ,

"DITURIA: A Framework for Decision Coordination Among Multiple Agents" by Helena Ibro, Geeta Mahala et al.

Decision making in multi-agent settings is a complex exercise where agents have to handle incomplete knowledge of the complete problem. Agents are interdependent in multi-agent decision making, being subject to the decisions of other agents who bring to bear other qualitative and quantitative criteria. Some aspects of this problem have been addressed in the Distributed Constraint Optimisation Problems (DCOP) and Markov Decision Processes literature. Taking inspiration from a medical example, our objective in this paper is to provide a framework to support multi-agent decision coordination. This method can be applied in scenarios where we seek to combine qualitative preferences on projected final states with assessment made using utility/objective functions, while accounting for partial agent knowledge. ....

Distributed Constraint Optimisation Problems , Markov Decision Processes , Adversarial Search , Ecision Coordination , Decision Optimisation ,

"Towards Antifragility in Contested Environments: Using Adversarial Sea" by Saad Sajid Hashmi, Hoa Khanh Dam et al.

Resilience and antifragility under duress present significant challenges for autonomic and self-adaptive systems operating in contested environments. In such settings, the system has to continually plan ahead, accounting for either an adversary or an environment that may negate its actions or degrade its capabilities. This will involve projecting future states, as well as assessing recovery options, counter-measures, and progress towards system goals. For antifragile systems to be effective, we envision three self-∗ properties to be of key importance: self-exploration, self-learning and self-training. Systems should be able to efficiently self-explore - using adversarial search - the potential impact of the adversary's attacks and compute the most resilient responses. The exploration can be assisted by prior knowledge of the adversary's capabilities and attack strategies, which can be self-learned - using opponent modelling - from previous attacks and interactions. The syst ....

Adversarial Search , Pponent Learning , Reinforcement Learning ,