Publication Library
Automating the Search for Artificial Life with Foundation Models
Description: With the recent Nobel Prize awarded for radical advances in protein discovery, foundation models (FMs) for exploring large combinatorial spaces promise to revolutionize many scientific fields. Artificial Life (ALife) has not yet integrated FMs, thus presenting a major opportunity for the field to alleviate the historical burden of relying chiefly on manual design and trial-and-error to discover the configurations of lifelike simulations. This paper presents, for the first time, a successful realization of this opportunity using vision-language FMs. The proposed approach, called Automated Search for Artificial Life (ASAL), (1) finds simulations that produce target phenomena, (2) discovers simulations that generate temporally open-ended novelty, and (3) illuminates an entire space of interestingly diverse simulations. Because of the generality of FMs, ASAL works effectively across a diverse range of ALife substrates including Boids, Particle Life, Game of Life, Lenia, and Neural Cellular Automata. A major result highlighting the potential of this technique is the discovery of previously unseen Lenia and Boids lifeforms, as well as cellular automata that are open-ended like Conway's Game of Life. Additionally, the use of FMs allows for the quantification of previously qualitative phenomena in a human-aligned way. This new paradigm promises to accelerate ALife research beyond what is possible through human ingenuity alone.
Created At: 17 January 2025
Updated At: 17 January 2025
Framework for Dynamic Situational Awareness in Human Robot Teams - An Interview Study
Description: In human-robot teams, human situational awareness is the operator's conscious knowledge of the team's states, actions, plans and their environment. Appropriate human situational awareness is critical to successful human-robot collaboration. In human-robot teaming, it is often assumed that the best and required level of situational awareness is knowing everything at all times. This view is problematic, because what a human needs to know for optimal team performance varies given the dynamic environmental conditions, task context and roles and capabilities of team members. We explore this topic by interviewing 16 participants with active and repeated experience in diverse human-robot teaming applications. Based on analysis of these interviews, we derive a framework explaining the dynamic nature of required situational awareness in human-robot teaming. In addition, we identify a range of factors affecting the dynamic nature of required and actual levels of situational awareness (i.e., dynamic situational awareness), types of situational awareness inefficiencies resulting from gaps between actual and required situational awareness, and their main consequences. We also reveal various strategies, initiated by humans and robots, that assist in maintaining the required situational awareness. Our findings inform the implementation of accurate estimates of dynamic situational awareness and the design of user-adaptive human-robot interfaces. Therefore, this work contributes to the future design of more collaborative and effective human-robot teams.
Created At: 17 January 2025
Updated At: 17 January 2025
Holoview - Interactive 3D visualization of medical data in AR
Description: We introduce HoloView, an innovative augmented reality (AR) system that enhances interactive learning of human anatomical structures through immersive visualization. Combining advanced rendering techniques with intuitive gesture-based interactions, HoloView provides a comprehensive technical solution for medical education. The system architecture features a distributed rendering pipeline that offloads stereoscopic computations to a remote server, optimizing performance and enabling high-quality visualization on less powerful devices. To prioritize visual quality in the user's direct line of sight while reducing computational load, we implement foveated rendering optimization, enhancing the immersive experience. Additionally, a hybrid surface-volume rendering technique is used to achieve faster rendering speeds without sacrificing visual fidelity. Complemented by a carefully designed user interface and gesture-based interaction system, HoloView allows users to naturally manipulate holographic content and seamlessly navigate the learning environment. HoloView significantly facilitates anatomical structure visualization and promotes an engaging, user-centric learning experience.
Created At: 17 January 2025
Updated At: 17 January 2025
Quantum Speedup for Nonreversible Markov Chains
Description: Quantum algorithms can potentially solve a handful of problems more efficiently than their classical counterparts. In that context, it has been discussed that Markov chains problems could be solved significantly faster using quantum computing. Indeed, previous work suggests that quantum computers could accelerate sampling from the stationary distribution of reversible Markov chains. However, in practice, certain physical processes of interest are nonreversible in the probabilistic sense and reversible Markov chains can sometimes be replaced by more efficient nonreversible chains targeting the same stationary distribution. This study uses modern quantum algorithmic techniques and Markov chain theory to sample from the stationary distribution of nonreversible Markov chains with faster worst-case runtime and without requiring the stationary distribution to be computed up to a multiplicative constant. Such an up-to-exponential quantum speedup goes beyond the predicted quadratic quantum acceleration for reversible chains and is likely to have a decisive impact on many applications ranging from statistics and machine learning to computational modeling in physics, chemistry, biology and finance.
Created At: 17 January 2025
Updated At: 17 January 2025
Simplifications to Guide Monte Carlo Tree Search in Combinatorial Games
Description: We examine a type of modified Monte Carlo Tree Search (MCTS) for strategising in combinatorial games. The modifications are derived by analysing simplified strategies and simplified versions of the underlying game and then using the results to construct an ensemble-type strategy. We present some instances where relative algorithm performance can be predicted from the results in the simplifications, making the approach useful as a heuristic for developing strategies in highly complex games, especially when simulation-type strategies and comparative analyses are largely intractable.
Created At: 16 January 2025
Updated At: 16 January 2025