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We present a multi-objective meta-search procedure that constructs candidate algorithms for state-space search puzzles like Rubik&#39;s cube. The candidate algorithms take the form of macro databases, i.e., rule tables that specify sequences of actions to perform in different states. Rules are repeatedly applied until the puzzle is solved. The objectives favor candidates that are god-like (solving the puzzle in fewer steps) and folk-like (having fewer rules in the macro database). We build each candidate with a non-deterministic rule table construction, and then optimize over the non-deterministic choice points to find candidates near the Pareto-optimal trades-offs between godliness and folksiness. We prove that the rule table construction is correct: it always terminates and solves every state at termination. This is verified empirically on the full 2x2x2 &quot;pocket&quot; cube, where correct (but unoptimized) constructions take under one hour and the total number of rules is less than 10% the number of possible states. We also empirically assess the multi-objective optimization on restricted variants of the cube with up to 29K possible states, showing relative improvements in the objectives between 14-20%. Avenues for scaling up the method in future work are discussed.

AAAI 2022

Towards Automated Discovery of God-Like Folk Algorithms for Rubik&#39;s Cube

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AAMAS 2021

We present a multi-objective meta-search procedure that constructs candidate algorithms for state-space search puzzles like Rubik's cube. The candidate algorithms take the form of macro databases, i.e., rule tables that specify sequences of actions to perform in different states. Rules are repeatedly applied until the puzzle is solved. The objectives favor candidates that are god-like (solving the puzzle in fewer steps) and folk-like (having fewer rules in the macro database). We build each candidate with a non-deterministic rule table construction, and then optimize over the non-deterministic choice points to find candidates near the Pareto-optimal trades-offs between godliness and folksiness. We prove that the rule table construction is correct: it always terminates and solves every state at termination. This is verified empirically on the full 2x2x2 "pocket" cube, where correct (but unoptimized) constructions take under one hour and the total number of rules is less than 10% the number of possible states. We also empirically assess the multi-objective optimization on restricted variants of the cube with up to 29K possible states, showing relative improvements in the objectives between 14-20%. Avenues for scaling up the method in future work are discussed.

Towards Automated Discovery of God-Like Folk Algorithms for Rubik's Cube

technical paper

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

In this work, we introduce 6D Convolutional Neural Network (6DCNN) designed to tackle the problem of detecting relative positions and orientations of local patterns when processing three-dimensional volumetric data. 6DCNN also includes SE(3)-equivariant message-passing and nonlinear activation operations constructed in the Fourier space. Working in the Fourier space allows significantly reducing the computational complexity of our operations. We demonstrate the properties of the 6D convolution and its efficiency in the recognition of spatial patterns. We also assess the 6DCNN model on several datasets from the recent CASP protein structure prediction challenges. Here, 6DCNN improves over the baseline architecture and also outperforms the state of the art.

6DCNN with Roto-Translational Convolution Filters for Volumetric Data Processing

We present a novel generalized zero-shot algorithm to recognize perceived emotions from gestures. Our task is to map gestures to novel emotion categories not encountered in training. We introduce an adversarial autoencoder-based representation learning that correlates 3D motion-captured gesture sequences with the vectorized representation of the natural-language perceived emotion terms using word2vec embeddings. The language-semantic embedding provides a representation of the emotion label space, and we leverage this underlying distribution to map the gesture sequences to the appropriate categorical emotion labels. We train our method using a combination of gestures annotated with known emotion terms and gestures not annotated with any emotions. We evaluate our method on the MPI Emotional Body Expressions Database (EBEDB) and obtain an accuracy of 58.43%. We see an improvement in performance compared to current state-of-the-art algorithms for generalized zero-shot learning by an absolute 25-27%. We also demonstrate our approach on publicly available online videos and movie scenes, where the actors' pose has been extracted and map to their respective emotive states.

Learning Unseen Emotions from Gestures via Semantically-Conditioned Zero-Shot Perception with Adversarial Autoencoders

The task of Legal Statute Identification (LSI) aims to identify the legal statutes that are relevant to a given description of facts or evidence of a legal case. 
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 We curate a large novel dataset for this task, including facts of cases from several major Indian Courts of Law, and statutes from the Indian Penal Code (IPC). 
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LeSICiN: A Heterogeneous Graph-Based Approach for Automatic Legal Statute Identification from Indian Legal Documents

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An Axiomatic Approach to Revising Preferences

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First Order Rewritability in Ontology-Mediated Querying in Horn Description Logics

Traditionally, clustered federated learning groups clients with the same data distribution into a cluster, so that every client is uniquely associated with one data distribution and helps train a model for this distribution. We relax this hard association assumption to soft clustered federated learning, which allows every local dataset to follow a mixture of multiple source distributions. We propose FedSoft, which trains both locally personalized models and high-quality cluster models in this setting. FedSoft limits client workload by using proximal updates to require the completion of only one optimization task from a subset of clients in every communication round. We show, analytically and empirically, that FedSoft effectively exploits similarities between the source distributions to learn personalized and cluster models that perform well.

FedSoft: Soft Clustered Federated Learning with Proximal Local Updating

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Equivalence in Argumentation Frameworks with a Claim-Centric View – Classical Results with Novel Ingredients

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Neural Networks Classify through the Class-Wise Means of Their Representations

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