2,713 research outputs found
Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures
Traditional robot task planning methods face challenges when dealing with
highly unstructured environments and complex tasks. We propose a task planning
method that combines human expertise with an LLM and have designed an LLM
prompt template, Think_Net_Prompt, with stronger expressive power to represent
structured professional knowledge. We further propose a method to progressively
decompose tasks and generate a task tree to reduce the planning volume for each
task, and we have designed a strategy to decouple robot task planning. By
dividing different planning entities and separating the task from the actual
machine binding process, the task planning process becomes more flexible.
Research results show that our method performs well in handling specified code
formats, understanding the relationship between tasks and subtasks, and
extracting parameters from text descriptions. However, there are also problems
such as limited complexity of task logic handling, ambiguity in the quantity of
parts and the precise location of assembly. Improving the precision of task
description and cognitive structure can bring certain improvements.
https://github.com/NOMIzy/Think_Net_Promp
Visualizing and Interacting with Concept Hierarchies
Concept Hierarchies and Formal Concept Analysis are theoretically well
grounded and largely experimented methods. They rely on line diagrams called
Galois lattices for visualizing and analysing object-attribute sets. Galois
lattices are visually seducing and conceptually rich for experts. However they
present important drawbacks due to their concept oriented overall structure:
analysing what they show is difficult for non experts, navigation is
cumbersome, interaction is poor, and scalability is a deep bottleneck for
visual interpretation even for experts. In this paper we introduce semantic
probes as a means to overcome many of these problems and extend usability and
application possibilities of traditional FCA visualization methods. Semantic
probes are visual user centred objects which extract and organize reduced
Galois sub-hierarchies. They are simpler, clearer, and they provide a better
navigation support through a rich set of interaction possibilities. Since probe
driven sub-hierarchies are limited to users focus, scalability is under control
and interpretation is facilitated. After some successful experiments, several
applications are being developed with the remaining problem of finding a
compromise between simplicity and conceptual expressivity
Problem hierarchies in continual learning
La recherche en apprentissage automatique peut ĂȘtre vue comme une quĂȘte vers lâaboutissement dâalgorithmes dâapprentissage de plus en plus gĂ©nĂ©raux, applicable Ă des problĂšmes de plus en plus rĂ©alistes. Selon cette perspective, le progrĂšs dans ce domaine peut ĂȘtre rĂ©alisĂ© de deux façons: par lâamĂ©lioration des mĂ©thodes algorithmiques associĂ©es aux problĂšmes existants, et par lâintroduction de nouveaux types de problĂšmes. Avec le progrĂšs marquĂ© du cĂŽtĂ© des mĂ©thodes dâapprentissage machine, une panoplie de nouveaux types de problĂšmes dâapprentissage ont aussi Ă©tĂ© proposĂ©s, oĂč les hypothĂšses de problĂšmes existants sont assouplies ou gĂ©nĂ©ralisĂ©es afin de mieux reflĂ©ter les conditions du monde rĂ©el. Le domaine de lâapprentissage en continu (Continual Learning) est un exemple dâun tel domaine, oĂč lâhypothĂšse de
la stationaritĂ© des distributions encourues lors de lâentrainement dâun modĂšles est assouplie, et oĂč les algorithmes dâapprentissages doivent donc sâadapter Ă des changements soudains ou progressifs dans leur environnement. Dans cet ouvrage, nous introduisons les hiĂ©rarchiĂ©es de problĂšmes, une application du concept de hiĂ©rarchie des types provenant des sciences informatiques, au domaine des problĂšmes de recherche en apprentissage machine. Les hierarchies de problĂšmes organisent et structurent les problĂšmes dâapprentissage en fonction de leurs hypothĂ©ses. Les mĂ©thodes peuvent donc dĂ©finir explicitement leur domaine dâapplication, leur permettant donc dâĂȘtre partagĂ©es et rĂ©utilisĂ©es Ă travers diffĂ©rent types de problĂšmes de maniĂšre polymorphique: Une mĂ©thode conçue pour un domaine donnĂ© peut aussi ĂȘtre appli-
quĂ©e Ă un domaine plus prĂ©cis que celui-ci, tel quâindiquĂ© par leur relation dans la hierarchie de problĂšmes. Nous dĂ©montrons que ce systĂšme, lorsque mis en oeuvre, comporte divers bienfaits qui addressent directement plusieurs des problĂšmes encourus par les chercheurs en apprentissage machine. Nous dĂ©montrons la viabilitĂ© de ce principe avec Sequoia, une
infrastructure logicielle libre qui implĂ©mente une hierarchie des problĂšmes en apprentissage continu. Nous espĂ©rons que ce nouveau paradigme, ainsi que sa premiĂšre implĂ©mentation, pourra servir Ă unifier et accĂ©lĂ©rer les divers efforts de recherche en apprentissage continu, ainsi quâĂ encourager des efforts similaires dans dâautres domaines de recherche. Vous pouvez nous aider Ă faire grandir lâarbre en visitant github.com/lebrice/Sequoia.Research in Machine Learning (ML) can be viewed as a quest to develop increasingly general
algorithmic solutions (methods) for increasingly challenging research problems (settings).
From this perspective, progress can be realized in two ways: by introducing better methods
for current settings, or by proposing interesting new settings for the research community to
solve. Alongside recent progress in methods, a wide variety of research settings have also been
introduced, often as variants of existing settings where underlying assumptions are removed
to make the problem more realistic or general. The field of Continual Learning (CL), for
example, consists of a family of settings where the stationarity assumption is removed, and
where methods as a result have to learn from environments or data distributions that can
change over time. In this work, we introduce the concept of problem hierarchies: hierarchical
structures in which research settings are systematically organized based on their assumptions.
Methods can then explicitly state their assumptions by selecting a target setting from this
hierarchy. Most importantly, these structures make it possible to easily share and reuse
research methods across different settings using inheritance, since a method developed for a
given setting is also directly applicable onto any of its children in the hierarchy. We argue
that this simple mechanism can have great implications for ML research in practice. As a
proof-of-concept of this approach, we introduce Sequoia, an open-source research framework
in which we construct a hierarchy of the settings and methods in CL. We hope that this
new paradigm and its first implementation can help unify and accelerate research in CL and
serve as inspiration for future work in other fields. You can help us grow the tree by visiting
github.com/lebrice/Sequoia
HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and Regime-Switch VAE
Factor model is a fundamental investment tool in quantitative investment,
which can be empowered by deep learning to become more flexible and efficient
in practical complicated investing situations. However, it is still an open
question to build a factor model that can conduct stock prediction in an online
and adaptive setting, where the model can adapt itself to match the current
market regime identified based on only point-in-time market information. To
tackle this problem, we propose the first deep learning based online and
adaptive factor model, HireVAE, at the core of which is a hierarchical latent
space that embeds the underlying relationship between the market situation and
stock-wise latent factors, so that HireVAE can effectively estimate useful
latent factors given only historical market information and subsequently
predict accurate stock returns. Across four commonly used real stock market
benchmarks, the proposed HireVAE demonstrate superior performance in terms of
active returns over previous methods, verifying the potential of such online
and adaptive factor model.Comment: Accepted to IJCAI 202
Some resonances between Eastern thought and Integral Biomathics in the framework of the WLIMES formalism for modelling living systems
Forty-two years ago, Capra published âThe Tao of Physicsâ (Capra, 1975). In this book (page 17) he writes: âThe exploration of the atomic and subatomic world in the twentieth century has âŠ. necessitated a radical revision of many of our basic conceptsâ and that, unlike âclassicalâ physics, the sub-atomic and quantum âmodern physicsâ shows resonances with Eastern thoughts and âleads us to a view of the world which is very similar to the views held by mystics of all ages and traditions.â This article stresses an analogous situation in biology with respect to a new theoretical approach for studying living systems, Integral Biomathics (IB), which also exhibits some resonances with Eastern thought. Stepping on earlier research in cybernetics1 and theoretical biology,2 IB has been developed since 2011 by over 100 scientists from a number of disciplines who have been exploring a substantial set of theoretical frameworks. From that effort, the need for a robust core model utilizing advanced mathematics and computation adequate for understanding the behavior of organisms as dynamic wholes was identified. At this end, the authors of this article have proposed WLIMES (Ehresmann and Simeonov, 2012), a formal theory for modeling living systems integrating both the Memory Evolutive Systems (Ehresmann and Vanbremeersch, 2007) and the Wandering Logic Intelligence (Simeonov, 2002b). Its principles will be recalled here with respect to their
resonances to Eastern thought
- âŠ