67 research outputs found
Expectation-Aware Planning: A Unifying Framework for Synthesizing and Executing Self-Explaining Plans for Human-Aware Planning
In this work, we present a new planning formalism called Expectation-Aware
planning for decision making with humans in the loop where the human's
expectations about an agent may differ from the agent's own model. We show how
this formulation allows agents to not only leverage existing strategies for
handling model differences but can also exhibit novel behaviors that are
generated through the combination of these different strategies. Our
formulation also reveals a deep connection to existing approaches in epistemic
planning. Specifically, we show how we can leverage classical planning
compilations for epistemic planning to solve Expectation-Aware planning
problems. To the best of our knowledge, the proposed formulation is the first
complete solution to decision-making in the presence of diverging user
expectations that is amenable to a classical planning compilation while
successfully combining previous works on explanation and explicability. We
empirically show how our approach provides a computational advantage over
existing approximate approaches that unnecessarily try to search in the space
of models while also failing to facilitate the full gamut of behaviors enabled
by our framework
Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (to STRIPS)
We achieved a new milestone in the difficult task of enabling agents to learn
about their environment autonomously. Our neuro-symbolic architecture is
trained end-to-end to produce a succinct and effective discrete state
transition model from images alone. Our target representation (the Planning
Domain Definition Language) is already in a form that off-the-shelf solvers can
consume, and opens the door to the rich array of modern heuristic search
capabilities. We demonstrate how the sophisticated innate prior we place on the
learning process significantly reduces the complexity of the learned
representation, and reveals a connection to the graph-theoretic notion of
"cube-like graphs", thus opening the door to a deeper understanding of the
ideal properties for learned symbolic representations. We show that the
powerful domain-independent heuristics allow our system to solve visual
15-Puzzle instances which are beyond the reach of blind search, without
resorting to the Reinforcement Learning approach that requires a huge amount of
training on the domain-dependent reward information.Comment: Accepted in IJCAI 2020 main track (accept ratio 12.6%). The prequel
of this paper, "The Search for STRIPS", can be found here: arXiv:1912.05492 .
(update, 2020/08/11) We expanded the related work sectio
Stable Model Counting and Its Application in Probabilistic Logic Programming
Model counting is the problem of computing the number of models that satisfy
a given propositional theory. It has recently been applied to solving inference
tasks in probabilistic logic programming, where the goal is to compute the
probability of given queries being true provided a set of mutually independent
random variables, a model (a logic program) and some evidence. The core of
solving this inference task involves translating the logic program to a
propositional theory and using a model counter. In this paper, we show that for
some problems that involve inductive definitions like reachability in a graph,
the translation of logic programs to SAT can be expensive for the purpose of
solving inference tasks. For such problems, direct implementation of stable
model semantics allows for more efficient solving. We present two
implementation techniques, based on unfounded set detection, that extend a
propositional model counter to a stable model counter. Our experiments show
that for particular problems, our approach can outperform a state-of-the-art
probabilistic logic programming solver by several orders of magnitude in terms
of running time and space requirements, and can solve instances of
significantly larger sizes on which the current solver runs out of time or
memory.Comment: Accepted in AAAI, 201
Characterizing and Computing All Delete-Relaxed Dead-ends
Dead-end detection is a key challenge in automated planning, and it is rapidly growing in popularity. Effective dead-end detection techniques can have a large impact on the strength of a planner, and so the effective computation of dead-ends is central to many planning approaches. One of the better understood techniques for detecting dead-ends is to focus on the delete relaxation of a planning problem, where dead-end detection is a polynomial-time operation. In this work, we provide a logical characterization for not just a single dead-end, but for every delete-relaxed dead-end in a planning problem. With a logical representation in hand, one could compile the representation into a form amenable to effective reasoning. We lay the ground-work for this larger vision and provide a preliminary evaluation to this en
Egocentric Planning for Scalable Embodied Task Achievement
Embodied agents face significant challenges when tasked with performing
actions in diverse environments, particularly in generalizing across object
types and executing suitable actions to accomplish tasks. Furthermore, agents
should exhibit robustness, minimizing the execution of illegal actions. In this
work, we present Egocentric Planning, an innovative approach that combines
symbolic planning and Object-oriented POMDPs to solve tasks in complex
environments, harnessing existing models for visual perception and natural
language processing. We evaluated our approach in ALFRED, a simulated
environment designed for domestic tasks, and demonstrated its high scalability,
achieving an impressive 36.07% unseen success rate in the ALFRED benchmark and
winning the ALFRED challenge at CVPR Embodied AI workshop. Our method requires
reliable perception and the specification or learning of a symbolic description
of the preconditions and effects of the agent's actions, as well as what object
types reveal information about others. It is capable of naturally scaling to
solve new tasks beyond ALFRED, as long as they can be solved using the
available skills. This work offers a solid baseline for studying end-to-end and
hybrid methods that aim to generalize to new tasks, including recent approaches
relying on LLMs, but often struggle to scale to long sequences of actions or
produce robust plans for novel tasks
Planutils: Bringing Planning to the Masses
PLANUTILS is a general library for setting up Linux-based environments for developing, running, and evaluating planners. Over the last decades, the planning community has produced countless solvers for various planning formalisms, as well as many other tools to help the planning practitioner. From state-of-the-art planners, over validators, to parsing libraries, the planning ecosystem has grown quite large. In the demo, we highlight an effort that aims to unify this ecosystem and make it seamless for users to get started with what the ICAPS community has to offer
Classical Planning in Deep Latent Space
Current domain-independent, classical planners require symbolic models of the
problem domain and instance as input, resulting in a knowledge acquisition
bottleneck. Meanwhile, although deep learning has achieved significant success
in many fields, the knowledge is encoded in a subsymbolic representation which
is incompatible with symbolic systems such as planners. We propose Latplan, an
unsupervised architecture combining deep learning and classical planning. Given
only an unlabeled set of image pairs showing a subset of transitions allowed in
the environment (training inputs), Latplan learns a complete propositional PDDL
action model of the environment. Later, when a pair of images representing the
initial and the goal states (planning inputs) is given, Latplan finds a plan to
the goal state in a symbolic latent space and returns a visualized plan
execution. We evaluate Latplan using image-based versions of 6 planning
domains: 8-puzzle, 15-Puzzle, Blocksworld, Sokoban and Two variations of
LightsOut.Comment: Under review at Journal of Artificial Intelligence Research (JAIR
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