220 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
Foundations of Human-Aware Planning -- A Tale of Three Models
abstract: A critical challenge in the design of AI systems that operate with humans in the loop is to be able to model the intentions and capabilities of the humans, as well as their beliefs and expectations of the AI system itself. This allows the AI system to be "human- aware" -- i.e. the human task model enables it to envisage desired roles of the human in joint action, while the human mental model allows it to anticipate how its own actions are perceived from the point of view of the human. In my research, I explore how these concepts of human-awareness manifest themselves in the scope of planning or sequential decision making with humans in the loop. To this end, I will show (1) how the AI agent can leverage the human task model to generate symbiotic behavior; and (2) how the introduction of the human mental model in the deliberative process of the AI agent allows it to generate explanations for a plan or resort to explicable plans when explanations are not desired. The latter is in addition to traditional notions of human-aware planning which typically use the human task model alone and thus enables a new suite of capabilities of a human-aware AI agent. Finally, I will explore how the AI agent can leverage emerging mixed-reality interfaces to realize effective channels of communication with the human in the loop.Dissertation/ThesisDoctoral Dissertation Computer Science 201
On Exploiting Hitting Sets for Model Reconciliation
In human-aware planning, a planning agent may need to provide an explanation
to a human user on why its plan is optimal. A popular approach to do this is
called model reconciliation, where the agent tries to reconcile the differences
in its model and the human's model such that the plan is also optimal in the
human's model. In this paper, we present a logic-based framework for model
reconciliation that extends beyond the realm of planning. More specifically,
given a knowledge base entailing a formula and a second
knowledge base not entailing it, model reconciliation seeks an
explanation, in the form of a cardinality-minimal subset of , whose
integration into makes the entailment possible. Our approach, based on
ideas originating in the context of analysis of inconsistencies, exploits the
existing hitting set duality between minimal correction sets (MCSes) and
minimal unsatisfiable sets (MUSes) in order to identify an appropriate
explanation. However, differently from those works targeting inconsistent
formulas, which assume a single knowledge base, MCSes and MUSes are computed
over two distinct knowledge bases. We conclude our paper with an empirical
evaluation of the newly introduced approach on planning instances, where we
show how it outperforms an existing state-of-the-art solver, and generic
non-planning instances from recent SAT competitions, for which no other solver
exists
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