Search Results for author: Achille Fokoue

Found 24 papers, 6 papers with code

Formally Specifying the High-Level Behavior of LLM-Based Agents

no code implementations12 Oct 2023 Maxwell Crouse, Ibrahim Abdelaziz, Ramon Astudillo, Kinjal Basu, Soham Dan, Sadhana Kumaravel, Achille Fokoue, Pavan Kapanipathi, Salim Roukos, Luis Lastras

We demonstrate how the proposed framework can be used to implement recent LLM-based agents (e. g., ReACT), and show how the flexibility of our approach can be leveraged to define a new agent with more complex behavior, the Plan-Act-Summarize-Solve (PASS) agent.

Question Answering

Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning

1 code implementation5 Jul 2023 Subhajit Chaudhury, Sarathkrishna Swaminathan, Daiki Kimura, Prithviraj Sen, Keerthiram Murugesan, Rosario Uceda-Sosa, Michiaki Tatsubori, Achille Fokoue, Pavan Kapanipathi, Asim Munawar, Alexander Gray

Text-based reinforcement learning agents have predominantly been neural network-based models with embeddings-based representation, learning uninterpretable policies that often do not generalize well to unseen games.

reinforcement-learning Representation Learning

An Ensemble Approach for Automated Theorem Proving Based on Efficient Name Invariant Graph Neural Representations

1 code implementation15 May 2023 Achille Fokoue, Ibrahim Abdelaziz, Maxwell Crouse, Shajith Ikbal, Akihiro Kishimoto, Guilherme Lima, Ndivhuwo Makondo, Radu Marinescu

NIAGRA addresses this problem by using 1) improved Graph Neural Networks for learning name-invariant formula representations that is tailored for their unique characteristics and 2) an efficient ensemble approach for automated theorem proving.

Automated Theorem Proving Transfer Learning

Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Description

no code implementations15 Sep 2022 Ruixuan Yan, Tengfei Ma, Achille Fokoue, Maria Chang, Agung Julius

In this study, we present Neuro-Symbolic Time Series Classification (NSTSC), a neuro-symbolic model that leverages signal temporal logic (STL) and neural network (NN) to accomplish TSC tasks using multi-view data representation and expresses the model as a human-readable, interpretable formula.

Interpretable Machine Learning Time Series +2

Expressive Reasoning Graph Store: A Unified Framework for Managing RDF and Property Graph Databases

1 code implementation13 Sep 2022 Sumit Neelam, Udit Sharma, Sumit Bhatia, Hima Karanam, Ankita Likhyani, Ibrahim Abdelaziz, Achille Fokoue, L. V. Subramaniam

Resource Description Framework (RDF) and Property Graph (PG) are the two most commonly used data models for representing, storing, and querying graph data.


A Benchmark for Generalizable and Interpretable Temporal Question Answering over Knowledge Bases

no code implementations15 Jan 2022 Sumit Neelam, Udit Sharma, Hima Karanam, Shajith Ikbal, Pavan Kapanipathi, Ibrahim Abdelaziz, Nandana Mihindukulasooriya, Young-suk Lee, Santosh Srivastava, Cezar Pendus, Saswati Dana, Dinesh Garg, Achille Fokoue, G P Shrivatsa Bhargav, Dinesh Khandelwal, Srinivas Ravishankar, Sairam Gurajada, Maria Chang, Rosario Uceda-Sosa, Salim Roukos, Alexander Gray, Guilherme Lima, Ryan Riegel, Francois Luus, L Venkata Subramaniam

Specifically, our benchmark is a temporal question answering dataset with the following advantages: (a) it is based on Wikidata, which is the most frequently curated, openly available knowledge base, (b) it includes intermediate sparql queries to facilitate the evaluation of semantic parsing based approaches for KBQA, and (c) it generalizes to multiple knowledge bases: Freebase and Wikidata.

Knowledge Base Question Answering Semantic Parsing

A Two-Stage Approach towards Generalization in Knowledge Base Question Answering

no code implementations10 Nov 2021 Srinivas Ravishankar, June Thai, Ibrahim Abdelaziz, Nandana Mihidukulasooriya, Tahira Naseem, Pavan Kapanipathi, Gaetano Rossiello, Achille Fokoue

Most existing approaches for Knowledge Base Question Answering (KBQA) focus on a specific underlying knowledge base either because of inherent assumptions in the approach, or because evaluating it on a different knowledge base requires non-trivial changes.

Knowledge Base Question Answering Knowledge Graphs +3

Learning to Guide a Saturation-Based Theorem Prover

no code implementations7 Jun 2021 Ibrahim Abdelaziz, Maxwell Crouse, Bassem Makni, Vernon Austil, Cristina Cornelio, Shajith Ikbal, Pavan Kapanipathi, Ndivhuwo Makondo, Kavitha Srinivas, Michael Witbrock, Achille Fokoue

In addition, to the best of our knowledge, TRAIL is the first reinforcement learning-based approach to exceed the performance of a state-of-the-art traditional theorem prover on a standard theorem proving benchmark (solving up to 17% more problems).

Automated Theorem Proving reinforcement-learning +1

An Experimental Study of Formula Embeddings for Automated Theorem Proving in First-Order Logic

no code implementations2 Feb 2020 Ibrahim Abdelaziz, Veronika Thost, Maxwell Crouse, Achille Fokoue

Automated theorem proving in first-order logic is an active research area which is successfully supported by machine learning.

Automated Theorem Proving

Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling

1 code implementation arXiv 2020 Maxwell Crouse, Ibrahim Abdelaziz, Cristina Cornelio, Veronika Thost, Lingfei Wu, Kenneth Forbus, Achille Fokoue

Recent advances in the integration of deep learning with automated theorem proving have centered around the representation of logical formulae as inputs to deep learning systems.

Automated Theorem Proving

Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks

no code implementations5 Nov 2019 Pavan Kapanipathi, Veronika Thost, Siva Sankalp Patel, Spencer Whitehead, Ibrahim Abdelaziz, Avinash Balakrishnan, Maria Chang, Kshitij Fadnis, Chulaka Gunasekara, Bassem Makni, Nicholas Mattei, Kartik Talamadupula, Achille Fokoue

A few approaches have shown that information from external knowledge sources like knowledge graphs (KGs) can add value, in addition to the textual content, by providing background knowledge that may be critical for a task.

Knowledge Graphs Natural Language Inference

Path-Based Contextualization of Knowledge Graphs for Textual Entailment

no code implementations5 Nov 2019 Kshitij Fadnis, Kartik Talamadupula, Pavan Kapanipathi, Haque Ishfaq, Salim Roukos, Achille Fokoue

In this paper, we introduce the problem of knowledge graph contextualization -- that is, given a specific NLP task, the problem of extracting meaningful and relevant sub-graphs from a given knowledge graph.

Knowledge Graphs Natural Language Inference

Answering Science Exam Questions Using Query Rewriting with Background Knowledge

no code implementations15 Sep 2018 Ryan Musa, Xiaoyan Wang, Achille Fokoue, Nicholas Mattei, Maria Chang, Pavan Kapanipathi, Bassem Makni, Kartik Talamadupula, Michael Witbrock

Open-domain question answering (QA) is an important problem in AI and NLP that is emerging as a bellwether for progress on the generalizability of AI methods and techniques.

Information Retrieval Multiple-choice +3

An effective algorithm for hyperparameter optimization of neural networks

no code implementations23 May 2017 Gonzalo Diaz, Achille Fokoue, Giacomo Nannicini, Horst Samulowitz

This paper addresses the problem of choosing appropriate parameters for the NN by formulating it as a box-constrained mathematical optimization problem, and applying a derivative-free optimization tool that automatically and effectively searches the parameter space.

Hyperparameter Optimization

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