In just seven minutes, the thieves took off with crown jewels containing with thousands of diamonds along with other precious gems. Police stand outside the Louvre after a brazen theft. Could the French TV series have been prophetic? The show envisioned a heist at the Louvre, an event that became reality on the morning of October 19, when a group of professional thieves managed to break into the world-famous Paris museum . In just seven minutes, they stole a host of priceless French crown jewels.

As a variant of the famous communication game Werewolf, One Night Ultimate W erewolf (ONUW) requires players to develop strategic discussion policies due to the potential role changes that increase the uncertainty and complexity of the game.

Reactive synthesis is classically modeled as a game, though often applied to domains such as arbiter circuits and communication protocols [1]. We aim to show how reactive synthesis can be applied to a literal game - cops and robbers - to generate strategies for agents in the game. We propose a game that requires the coordination of multiple agents in a space of datatypes and operations that are richer than is easily captured by the traditional Linear Temporal Logic (LTL) approach of synthesis over Boolean streams [2]. In particular, we draw inspiration from prior work on Coordination Synthesis [3], LTL moduluo theories (LTLt) [4], and Temporal Stream Logic Moduluo theories (TSL-MT) [5, 6] to describe our problem and potential solution spaces. The traditional game [7] asks whether K cops can catch a single robber on a graph. In a temporal logic setting, this amounts to a safety condition on the robbers (they are never caught by the cops), and the dual liveness condition for the cops (they eventually catch the robbers). We modify the traditional graph theory focused version of the game to have a more visual game on a grid system, allowing for various configurations, including: An environment with various node types such as walls, safe zones, and open spaces.

More and more people are learning about the world through chatbots and the software's kin, whether they mean to or not. Google has rolled out generative AI to users of its search engine on at least four continents, placing AI-written responses above the usual list of links; as many as 1 billion people may encounter this feature by the end of the year. Meta's AI assistant has been integrated into Facebook, Messenger, WhatsApp, and Instagram, and is sometimes the default option when a user taps the search bar. And Apple is expected to integrate generative AI into Siri, Mail, Notes, and other apps this fall. Less than two years after ChatGPT's launch, bots are quickly becoming the default filters for the web.

This study examines the impact of AI on human false memories -- recollections of events that did not occur or deviate from actual occurrences. It explores false memory induction through suggestive questioning in Human-AI interactions, simulating crime witness interviews. Four conditions were tested: control, survey-based, pre-scripted chatbot, and generative chatbot using a large language model (LLM). Participants (N=200) watched a crime video, then interacted with their assigned AI interviewer or survey, answering questions including five misleading ones. False memories were assessed immediately and after one week. Results show the generative chatbot condition significantly increased false memory formation, inducing over 3 times more immediate false memories than the control and 1.7 times more than the survey method. 36.4% of users' responses to the generative chatbot were misled through the interaction. After one week, the number of false memories induced by generative chatbots remained constant. However, confidence in these false memories remained higher than the control after one week. Moderating factors were explored: users who were less familiar with chatbots but more familiar with AI technology, and more interested in crime investigations, were more susceptible to false memories. These findings highlight the potential risks of using advanced AI in sensitive contexts, like police interviews, emphasizing the need for ethical considerations.

Communication is a fundamental aspect of human society, facilitating the exchange of information and beliefs among people. Despite the advancements in large language models (LLMs), recent agents built with these often neglect the control over discussion tactics, which are essential in communication scenarios and games. As a variant of the famous communication game Werewolf, One Night Ultimate Werewolf (ONUW) requires players to develop strategic discussion policies due to the potential role changes that increase the uncertainty and complexity of the game. In this work, we first present the existence of the Perfect Bayesian Equilibria (PBEs) in two scenarios of the ONUW game: one with discussion and one without. The results showcase that the discussion greatly changes players' utilities by affecting their beliefs, emphasizing the significance of discussion tactics. Based on the insights obtained from the analyses, we propose an RL-instructed language agent framework, where a discussion policy trained by reinforcement learning (RL) is employed to determine appropriate discussion tactics to adopt. Our experimental results on several ONUW game settings demonstrate the effectiveness and generalizability of our proposed framework.

In search applications, autonomous unmanned vehicles must be able to efficiently reacquire and localize mobile targets that can remain out of view for long periods of time in large spaces. As such, all available information sources must be actively leveraged -- including imprecise but readily available semantic observations provided by humans. To achieve this, this work develops and validates a novel collaborative human-machine sensing solution for dynamic target search. Our approach uses continuous partially observable Markov decision process (CPOMDP) planning to generate vehicle trajectories that optimally exploit imperfect detection data from onboard sensors, as well as semantic natural language observations that can be specifically requested from human sensors. The key innovation is a scalable hierarchical Gaussian mixture model formulation for efficiently solving CPOMDPs with semantic observations in continuous dynamic state spaces. The approach is demonstrated and validated with a real human-robot team engaged in dynamic indoor target search and capture scenarios on a custom testbed.

In order to use computational intelligence for automated narrative synthesis, domain knowledge of the story world must be defined, a task which is currently confined to experts. This paper discusses the benefits and tradeoffs between agent-centric and event-centric approaches towards authoring the domain knowledge of story worlds. In an effort to democratize story world creation, we present an accessible graphical platform for content creators and even end users to create their own story worlds, populate it with smart characters and objects, and define narrative events that can be used by existing tools for automated narrative synthesis. We demonstrate the potential of our system by authoring a simple bank robbery story world, and integrate it with existing solutions for event-centric planning to synthesize example digital stories.

Tree projections provide a mathematical framework that encompasses all the various (purely) structural decomposition methods that have been proposed in the literature to single out classes of nearly-acyclic (hyper)graphs, such as the tree decomposition method, which is the most powerful decomposition method on graphs, and the (generalized) hypertree decomposition method, which is its natural counterpart on arbitrary hypergraphs. The paper analyzes this framework, by focusing in particular on "minimal" tree projections, that is, on tree projections without useless redundancies. First, it is shown that minimal tree projections enjoy a number of properties that are usually required for normal form decompositions in various structural decomposition methods. In particular, they enjoy the same kind of connection properties as (minimal) tree decompositions of graphs, with the result being tight in the light of the negative answer that is provided to the open question about whether they enjoy a slightly stronger notion of connection property, defined to speed-up the computation of hypertree decompositions. Second, it is shown that tree projections admit a natural game-theoretic characterization in terms of the Captain and Robber game. In this game, as for the Robber and Cops game characterizing tree decompositions, the existence of winning strategies implies the existence of monotone ones. As a special case, the Captain and Robber game can be used to characterize the generalized hypertree decomposition method, where such a game-theoretic characterization was missing and asked for. Besides their theoretical interest, these results have immediate algorithmic applications both for the general setting and for structural decomposition methods that can be recast in terms of tree projections.