33 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
Planning for Attacker Entrapment in Adversarial Settings
In this paper, we propose a planning framework to generate a defense strategy
against an attacker who is working in an environment where a defender can
operate without the attacker's knowledge. The objective of the defender is to
covertly guide the attacker to a trap state from which the attacker cannot
achieve their goal. Further, the defender is constrained to achieve its goal
within K number of steps, where K is calculated as a pessimistic lower bound
within which the attacker is unlikely to suspect a threat in the environment.
Such a defense strategy is highly useful in real world systems like honeypots
or honeynets, where an unsuspecting attacker interacts with a simulated
production system while assuming it is the actual production system. Typically,
the interaction between an attacker and a defender is captured using game
theoretic frameworks. Our problem formulation allows us to capture it as a much
simpler infinite horizon discounted MDP, in which the optimal policy for the
MDP gives the defender's strategy against the actions of the attacker. Through
empirical evaluation, we show the merits of our problem formulation
Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning
There is a growing interest in applying pre-trained large language models
(LLMs) to planning problems. However, methods that use LLMs directly as
planners are currently impractical due to several factors, including limited
correctness of plans, strong reliance on feedback from interactions with
simulators or even the actual environment, and the inefficiency in utilizing
human feedback. In this work, we introduce a novel alternative paradigm that
constructs an explicit world (domain) model in planning domain definition
language (PDDL) and then uses it to plan with sound domain-independent
planners. To address the fact that LLMs may not generate a fully functional
PDDL model initially, we employ LLMs as an interface between PDDL and sources
of corrective feedback, such as PDDL validators and humans. For users who lack
a background in PDDL, we show that LLMs can translate PDDL into natural
language and effectively encode corrective feedback back to the underlying
domain model. Our framework not only enjoys the correctness guarantee offered
by the external planners but also reduces human involvement by allowing users
to correct domain models at the beginning, rather than inspecting and
correcting (through interactive prompting) every generated plan as in previous
work. On two IPC domains and a Household domain that is more complicated than
commonly used benchmarks such as ALFWorld, we demonstrate that GPT-4 can be
leveraged to produce high-quality PDDL models for over 40 actions, and the
corrected PDDL models are then used to successfully solve 48 challenging
planning tasks. Resources including the source code will be released at:
https://guansuns.github.io/pages/llm-dm