ReplicaCADisonehousewith 111 layouts (6 fromrealscans, 1 05 artistcreated), The TDWTransporter Challenge [7] has 15 houses, ManipulaTHOR [33] has 30 rooms, RoomRfrom Visual Room Rearrangement [58] has 120 rooms, iGibson [36] has 15 houses, and VirtualHome [51] has 6 houses.

In Model-based Reinforcement Learning (MBRL), model learning is critical since an inaccurate model can bias policy learning via generating misleading samples. However, learning an accurate model can be difficult since the policy is continually updated and the induced distribution over visited states used for model learning shifts accordingly. Prior methods alleviate this issue by quantifying the uncertainty of model-generated samples. However, these methods only quantify the uncertainty passively after the samples were generated, rather than foreseeing the uncertainty before model trajectories fall into those highly uncertain regions. The resulting low-quality samples can induce unstable learning targets and hinder the optimization of the policy. Moreover, while being learned to minimize one-step prediction errors, the model is generally used to predict for multiple steps, leading to a mismatch between the objectives of model learning and model usage.

Many Say They're Not Involved A presentation that has been shared with the Trump administration references Tesla, Ikea, TSMC, and more in its plan to rebuild Gaza. Some of these companies say they had no idea they were mentioned. The mound of rubble at the site of the Unknown Soldier Tower, destroyed by overnight Israeli bombardment, is pictured in the Rimal neighbourhood of Gaza City on September 15, 2025. A sweeping plan to reconstruct Gaza, which has been shared with Trump administration officials, features the names and logos of more than two dozen companies--some of which tell WIRED they had no knowledge they were named or involved. The presentation outlining the plan was reportedly created by some of the businessmen who helped ideate what became the controversial nonprofit the Gaza Humanitarian Foundation, which is currently leading aid distribution in Gaza, calling for the creation of a new entity called the Gaza Reconstitution, Economic Acceleration and Transformation (GREAT) Trust.

Additional Feedback: major points: lines 234-246: what is "fan in" and "fan out", this paragraph doesn't explain what it is? Does it relate to neural network architecture (shown in Figure 1)? I have no idea what "large fan-in" and "small fan-out" means. Is channeling a bottleneck in the state space? If so, where is this?

Check out all the on-demand sessions from the Intelligent Security Summit here. From its inception in 2015, OpenAI has always made it clear that its central goal is to build artificial general intelligence (AGI). Its stated mission is "to ensure that artificial general intelligence benefits all of humanity." This past Friday, OpenAI CEO Sam Altman authored a blog post titled "Planning for AGI and Beyond," which discussed how the company believes the world can prepare for AGI, both in the short and long term. Some found the blog post, which has a million "likes" on Twitter alone, "fascinating."

On August 9, 2019, the National Institute of Standards and Technology (NIST) released "U.S. Leadership in AI: A Plan for Federal Engagement in Developing.

Drew McDermott Research on planning for robots is in such a state of flux that there is disagreement about what planning is and whether it is necessary. We can take planning to be the optimization and debugging of a robot's program by reasoning about possible courses of execution. It is necessary to the extent that fragments of robot programs are combined at run time. There are several strands of research in the field; I survey six: (1) attempts to avoid planning; (2) the design of flexible plan notations; (3) theories of time-constrained planning; (4) planning by projecting and repairing faulty plans; (5) motion planning; and (6) the learning of optimal behaviors from reinforcements. More research is needed on formal semantics for robot plans.

I. Introduction Two premises, reflected in the title, underlie the perspective from which I will consider research in natural language processing in this paper.* First, progress on building computer systems that process natural languages in any meaningful sense (i.e., systems that interact reasonably with people in natural language) requires considering language as part of a larger communicative situation. In this larger situation, the participants in a conversation and their states of mind are as important to the interpretation of an utterance as the linguistic expressions from which it is formed. A central concern when language is considered as communication is its function in building and using shared models of the world. Indeed, the notion of a shared model is inherent in the word "communicate," which is derived from the Latin communi Preparation of this paper was supported by the National Science Foundation under Grant No. MCS76-220004, and the Defense Advanced Research Projects Agency under Contract N00039-79C0118 with the Naval Electronic Systems Command.

There's More to Life Than Making Plans For many years, research in AI plan generation was governed by a number of strong, simplifying assumptions: The planning agent is omniscient, its actions are deterministic and instantaneous, its goals are fixed and categorical, and its environment is static. More recently, researchers have developed expanded planning algorithms that are not predicated on such assumptions, but changing the way in which plans are formed is only part of what is required when the classical assumptions are abandoned. The demands of dynamic, uncertain environments mean that in addition to being able to form plans--even probabilistic, uncertain plans--agents must be able to effectively manage their plans. In this article, which is based on a talk given at the 1998 AAAI Fall Symposium on Distributed, Continual Planning, we first identify reasoning tasks that are involved in plan management, including commitment management, environment monitoring, alternative assessment, plan elaboration, metalevel control, and coordination with other agents. We next survey approaches we have developed to many of these tasks and discuss a plan-management system we are building to ground our theoretical work, by providing us with a platform for integrating our techniques and exploring their value in a realistic problem.

The recent advances in computer speed and algorithms for probabilistic inference have led to a resurgence of work on planning under uncertainty. The aim is to design AI planners for environments where there might be incomplete or faulty information, where actions might not always have the same results, and where there might be tradeoffs between the different possible outcomes of a plan. Addressing uncertainty in AI, planning algorithms will greatly increase the range of potential applications, but there is plenty of work to be done before we see practical decision-theoretic planning systems. This article outlines some of the challenges that need to be overcome and surveys some of the recent work in the area. In problems where actions can lead to a number of different possible outcomes, or where the benefits of executing a plan must be weighed against the costs, the framework of decision theory can be used to compare alternative plans.