Search Results for author: Christian Muise

Found 13 papers, 2 papers with code

MACQ: A Holistic View of Model Acquisition Techniques

1 code implementation14 Jun 2022 Ethan Callanan, Rebecca De Venezia, Victoria Armstrong, Alison Paredes, Tathagata Chakraborti, Christian Muise

For over three decades, the planning community has explored countless methods for data-driven model acquisition.

Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief

no code implementations6 Oct 2021 Christian Muise, Vaishak Belle, Paolo Felli, Sheila Mcilraith, Tim Miller, Adrian R. Pearce, Liz Sonenberg

Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents.

A Permutation-Invariant Representation of Neural Networks with Neuron Embeddings

no code implementations29 Sep 2021 Ryan Zhou, Christian Muise, Ting Hu

A key property of this representation is that there are multiple representations of a network which can be obtained by permuting the order of the neurons.

Transfer Learning

Classical Planning in Deep Latent Space

1 code implementation30 Jun 2021 Masataro Asai, Hiroshi Kajino, Alex Fukunaga, Christian Muise

Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck.

Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (to STRIPS)

no code implementations27 Apr 2020 Masataro Asai, Christian Muise

We achieved a new milestone in the difficult task of enabling agents to learn about their environment autonomously.

Planning for Goal-Oriented Dialogue Systems

no code implementations17 Oct 2019 Christian Muise, Tathagata Chakraborti, Shubham Agarwal, Ondrej Bajgar, Arunima Chaudhary, Luis A. Lastras-Montano, Josef Ondrej, Miroslav Vodolan, Charlie Wiecha

Generating complex multi-turn goal-oriented dialogue agents is a difficult problem that has seen a considerable focus from many leaders in the tech industry, including IBM, Google, Amazon, and Microsoft.

Goal-Oriented Dialogue Systems slot-filling +1

Expectation-Aware Planning: A Unifying Framework for Synthesizing and Executing Self-Explaining Plans for Human-Aware Planning

no code implementations18 Mar 2019 Sarath Sreedharan, Tathagata Chakraborti, Christian Muise, Subbarao Kambhampati

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.

Decision Making

Finite LTL Synthesis is EXPTIME-complete

no code implementations14 Sep 2016 Jorge A. Baier, Alberto Camacho, Christian Muise, Sheila A. McIlraith

LTL synthesis -- the construction of a function to satisfy a logical specification formulated in Linear Temporal Logic -- is a 2EXPTIME-complete problem with relevant applications in controller synthesis and a myriad of artificial intelligence applications.

Social planning for social HRI

no code implementations21 Feb 2016 Liz Sonenberg, Tim Miller, Adrian Pearce, Paolo Felli, Christian Muise, Frank Dignum

Making a computational agent 'social' has implications for how it perceives itself and the environment in which it is situated, including the ability to recognise the behaviours of others.

Projected Model Counting

no code implementations28 Jul 2015 Rehan Abdul Aziz, Geoffrey Chu, Christian Muise, Peter Stuckey

The task is to compute the number of assignments to P such that there exists an extension to 'non-priority' variables V\P that satisfies F. Projected model counting arises when some parts of the model are irrelevant to the counts, in particular when we require additional variables to model the problem we are counting in SAT.

Stable Model Counting and Its Application in Probabilistic Logic Programming

no code implementations20 Nov 2014 Rehan Abdul Aziz, Geoffrey Chu, Christian Muise, Peter Stuckey

Model counting is the problem of computing the number of models that satisfy a given propositional theory.

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