Planning is a pivotal ability of any intelligent system being developed for real-world applications. AI planning is concerned with researching and developing planning systems that automatically compute plans that satisfy some user objective. Identifying and understanding the relevant and realistic aspects that characterise real-world application domains are crucial to the development of AI planning systems. This provides guidance to knowledge engineers and software engineers in the process of designing, identifying, and categorising resources required for the development process. To the best of our knowledge, such support does not exist. We address this research gap by developing a conceptual framework that identifies and categorises the aspects of real-world planning domains in varying levels of granularity. Our framework provides not only a common terminology but also a comprehensive overview of a broad range of planning aspects exemplified using the domain of sustainable buildings as a prominent application domain of AI planning. The framework has the potential to impact the design, development, and applicability of AI planning systems in real-world application domains.
University of Cambridge（剑桥大学）；Ecole Centrale Paris（巴黎中央理工学院）
We are dealing with the problem of space layout planning here. We present an architectural conceptual CAD approach. Starting with design specifications in terms of constraints over spaces, a specific enumeration heuristics leads to a complete set of consistent conceptual design solutions named topological solutions. These topological solutions which do not presume any precise definitive dimension correspond to the sketching step that an architect carries out from the Design specifications on a preliminary design phase in architecture.
University of Amsterdam（阿姆斯特丹大学）；UC Berkeley（加州大学伯克利分校）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；University of Tübingen（蒂宾根大学）
An agent who interacts with a wide population of other agents needs to be aware that there may be variations in their understanding of the world. Furthermore, the machinery which they use to perceive may be inherently different, as is the case between humans and machines. In this work, we present both an image reference game between a speaker and a population of listeners where reasoning about the concepts other agents can comprehend is necessary and a model formulation with this capability. We focus on reasoning about the conceptual understanding of others, as well as adapting to novel gamepl…
This paper introduces abstractions that are meaningful for computers and that can be built and used according to computers' own criteria, i.e., computable abstractions. It is analyzed how abstractions can be seen to serve as the building blocks for the creation of one own's understanding of things, which is essential in performing intellectual tasks. Thus, abstractional machines are defined, which following a mechanical process can, based on computable abstractions, build and use their own understanding of things. Abstractional machines are illustrated through an example that outlines their ap…
School of Information Technology, Vellore Institute of Technology（韦洛尔理工学院信息技术学院）
Human cognition is a complex process facilitated by the intricate architecture of human brain. However, human cognition is often reduced to quantum theory based events in principle because of their correlative conjectures for the purpose of analysis for reciprocal understanding. In this paper, we begin our analysis of human cognition via formal methods and proceed towards quantum theories. Human cognition often violate classic probabilities on which formal representation of conceptual spaces are built. Further, geometric representation of conceptual spaces proposed by Gardenfors discusses the …
Understanding large ontologies is still an issue, and has an impact on many ontology engineering tasks. We describe a novel method for identifying and extracting conceptual components from domain ontologies, which are used to understand and compare them. The method is applied to two corpora of ontologies in the Cultural Heritage and Conference domain, respectively. The results, which show good quality, are evaluated by manual inspection and by correlation with datasets and tool performance from the ontology alignment evaluation initiative.
ChronoGraph: Functional 4D Scene Graphs with Vision-Language Models for Interaction Understanding and Grounded Planning
ETH Zurich（苏黎世联邦理工学院）；Technical University of Munich（慕尼黑工业大学）；University of British Columbia（不列颠哥伦比亚大学）；University of California, Berkeley（加州大学伯克利分校）；Google（谷歌）；Microsoft（微软）
Partially observable Markov decision processes (POMDPs) are a natural model for planning problems where effects of actions are nondeterministic and the state of the world is not completely observable. It is difficult to solve POMDPs exactly. This paper proposes a new approximation scheme. The basic idea is to transform a POMDP into another one where additional information is provided by an oracle. The oracle informs the planning agent that the current state of the world is in a certain region. The transformed POMDP is consequently said to be region observable. It is easier to solve than the or…
A long-held objective in AI is to build systems that understand concepts in a humanlike way. Setting aside the difficulty of building such a system, even trying to evaluate one is a challenge, due to present-day AI's relative opacity and its proclivity for finding shortcut solutions. This is exacerbated by humans' tendency to anthropomorphize, assuming that a system that can recognize one instance of a concept must also understand other instances, as a human would. In this paper, we argue that understanding a concept requires the ability to use it in varied contexts. Accordingly, we propose sy…
MineAnyBuild: Benchmarking Spatial Planning for Open-world AI Agents
Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；Shanghai Jiao Tong University（上海交通大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Huawei Noah’s Ark Lab（华为诺亚实验室）
Okinawa Institute of Science and Technology（冲绳科学技术大学院大学）
We show that goal-directed action planning and generation in a teleological framework can be formulated using the free energy principle. The proposed model, which is built on a variational recurrent neural network model, is characterized by three essential features. These are that (1) goals can be specified for both static sensory states, e.g., for goal images to be reached and dynamic processes, e.g., for moving around an object, (2) the model can not only generate goal-directed action plans, but can also understand goals by sensory observation, and (3) the model generates future action plans…
PhyBlock: A Progressive Benchmark for Physical Understanding and Planning via 3D Block Assembly
Mohamed bin Zayed University of Artificial Intelligence（莫扎德·本·扎耶德人工智能大学）；Tsinghua Shenzhen International Graduate School, Tsinghua University, China（清华大学深圳国际研究生院）；Shenzhen Campus of Sun Yat-sen University（孙中山大学深圳校区）；Spatialtemporal AI（时空人工智能）；Shanghai Jiao Tong University（上海交通大学）
MIT-IBM Watson AI Lab（麻省理工-IBM沃森人工智能实验室）；IBM Research（IBM研究院）
We propose an unsupervised neural model for learning a discrete embedding of words. Unlike existing discrete embeddings, our binary embedding supports vector arithmetic operations similar to continuous embeddings. Our embedding represents each word as a set of propositional statements describing a transition rule in classical/STRIPS planning formalism. This makes the embedding directly compatible with symbolic, state of the art classical planning solvers.
A neural network for modeling human concept formation, understanding and communication
Laboratory of Brain Atlas and Brain-inspired Intelligence（脑图谱与脑启发智能实验室）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Future Technology（未来技术学院）；University of Chinese Academy of Sciences（中国科学院大学）；State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology（脑认知与脑启发智能技术国家重点实验室）；School of Psychological and Cognitive Sciences & Beijing Key Laboratory of Behavior and Mental Health（心理学与认知科学学院及北京行为与心理健康重点实验室）；IDG/McGovern Institute for Brain Research（IDG/麦戈文脑科学研究院）；Institute for Artificial Intelligence（人工智能研究所）；Key Laboratory of Machine Perception (Ministry of Education)（机器感知重点实验室）
A Categorical Representation Language and Computational System for Knowledge-Based Planning
University of Maryland（马里兰大学）；Johns Hopkins University Applied Physics Laboratory（约翰斯·霍普金斯大学应用物理实验室）；Topos Institute（拓扑斯研究所）；University of Florida（佛罗里达大学）
Conceptual modeling (CM) applies abstraction to reduce the complexity of a system under study (e.g., an excerpt of reality). As a result of the conceptual modeling process a human interpretable, formalized representation (i.e., a conceptual model) is derived which enables understanding and communication among humans, and processing by machines. Artificial Intelligence (AI) algorithms are also applied to complex realities (regularly represented by vast amounts of data) to identify patterns or to classify entities in the data. Aside from the commonalities of both approaches, a significant differ…
Human social behavior is structured by relationships. We form teams, groups, tribes, and alliances at all scales of human life. These structures guide multi-agent cooperation and competition, but when we observe others these underlying relationships are typically unobservable and hence must be inferred. Humans make these inferences intuitively and flexibly, often making rapid generalizations about the latent relationships that underlie behavior from just sparse and noisy observations. Rapid and accurate inferences are important for determining who to cooperate with, who to compete with, and ho…
Humans exhibit incredibly high levels of multi-modal understanding - combining visual cues with read, or heard knowledge comes easy to us and allows for very accurate interaction with the surrounding environment. Various simulation environments focus on providing data for tasks related to scene understanding, question answering, space exploration, visual navigation. In this work, we are providing a solution to encompass both, visual and behavioural aspects of simulation in a new environment for learning interactive reasoning in manipulation setup. SAMPLE-HD environment allows to generate vario…
