Zobrazeno 1 - 10
of 269
pro vyhledávání: '"Riedl, Mark"'
Autor:
Mansi, Gennie, Riedl, Mark
Laws play a key role in the complex socio-technical system impacting contestability: they create the regulations shaping the way AI systems are designed, evaluated, and used. Despite their role in the AI value chain, lawyers' impact on contestability
Externí odkaz:
http://arxiv.org/abs/2409.17626
World models, which encapsulate the dynamics of how actions affect environments, are foundational to the functioning of intelligent agents. In this work, we explore the potential of Large Language Models (LLMs) to operate as world models. Although LL
Externí odkaz:
http://arxiv.org/abs/2409.12278
Generative Artificial Intelligence (AI) encounters limitations in efficiency and fairness within the realm of Procedural Content Generation (PCG) when human creators solely drive and bear responsibility for the generative process. Alternative setups,
Externí odkaz:
http://arxiv.org/abs/2409.16291
Autor:
Ehsan, Upol, Riedl, Mark O.
When the initial vision of Explainable (XAI) was articulated, the most popular framing was to open the (proverbial) "black-box" of AI so that we could understand the inner workings. With the advent of Large Language Models (LLMs), the very ability to
Externí odkaz:
http://arxiv.org/abs/2408.05345
Interpretability of deep reinforcement learning systems could assist operators with understanding how they interact with their environment. Vector quantization methods -- also called codebook methods -- discretize a neural network's latent space that
Externí odkaz:
http://arxiv.org/abs/2407.19532
Unlike reinforcement learning (RL) agents, humans remain capable multitaskers in changing environments. In spite of only experiencing the world through their own observations and interactions, people know how to balance focusing on tasks with learnin
Externí odkaz:
http://arxiv.org/abs/2407.00264
Open-ended worlds are those in which there are no pre-specified goals or environmental reward signal. As a consequence, an agent must know how to perform a multitude of tasks. However, when a new task is presented to an agent, we expect it to be able
Externí odkaz:
http://arxiv.org/abs/2405.06059
Autor:
Balloch, Jonathan C., Bhagat, Rishav, Zollicoffer, Geigh, Jia, Ruoran, Kim, Julia, Riedl, Mark O.
In deep reinforcement learning (RL) research, there has been a concerted effort to design more efficient and productive exploration methods while solving sparse-reward problems. These exploration methods often share common principles (e.g., improving
Externí odkaz:
http://arxiv.org/abs/2404.02235
Autor:
Xie, Kaige, Riedl, Mark
Automated story generation has been one of the long-standing challenges in NLP. Among all dimensions of stories, suspense is very common in human-written stories but relatively under-explored in AI-generated stories. While recent advances in large la
Externí odkaz:
http://arxiv.org/abs/2402.17119
Autor:
Desai, Deven R., Riedl, Mark
Copyright and computer science continue to intersect and clash, but they can coexist. The advent of new technologies such as digitization of visual and aural creations, sharing technologies, search engines, social media offerings, and more challenge
Externí odkaz:
http://arxiv.org/abs/2403.14653