activity
20142024
most citedScaling up Heuristic Planning with Relational Decision Trees

30 citations · 49 across the 15 of their papers we have counts for

collaborators

15 papers

cs.AI2024

Intelligent Execution through Plan Analysis

Daniel Borrajo, Manuela Veloso

Intelligent robots need to generate and execute plans. In order to deal with the complexity of real environments, planning makes some assumptions about the world. When executing pl…

cs.IR2024

Methods for Matching English Language Addresses

Keshav Ramani, Daniel Borrajo

Addresses occupy a niche location within the landscape of textual data, due to the positional importance carried by every word, and the geographical scope it refers to. The task of…

cs.AI2023

Contrastive Explanations of Centralized Multi-agent Optimization Solutions

Parisa Zehtabi, Alberto Pozanco, Ayala Bloch +2

In many real-world scenarios, agents are involved in optimization problems. Since most of these scenarios are over-constrained, optimal solutions do not always satisfy all agents.…

cs.LG2023★ 5 cited

A Look into Causal Effects under Entangled Treatment in Graphs: Investigating the Impact of Contact on MRSA Infection

Jing Ma, Chen Chen, Anil Vullikanti +4

Methicillin-resistant Staphylococcus aureus (MRSA) is a type of bacteria resistant to certain antibiotics, making it difficult to prevent MRSA infections. Among decades of efforts…

cs.LG2023★ 7 cited

On the Constrained Time-Series Generation Problem

Andrea Coletta, Sriram Gopalakrishan, Daniel Borrajo +1

Synthetic time series are often used in practical applications to augment the historical time series dataset for better performance of machine learning algorithms, amplify the occu…

cs.AI2022★ 1 cited

Fairness in Multi-Agent Planning

Alberto Pozanco, Daniel Borrajo

In cooperative Multi-Agent Planning (MAP), a set of goals has to be achieved by a set of agents. Independently of whether they perform a pre-assignment of goals to agents or they d…

cs.LG2022

Inapplicable Actions Learning for Knowledge Transfer in Reinforcement Learning

Leo Ardon, Alberto Pozanco, Daniel Borrajo +1

Reinforcement Learning (RL) algorithms are known to scale poorly to environments with many available actions, requiring numerous samples to learn an optimal policy. The traditional…

cs.AI2022

Anticipatory Counterplanning

Alberto Pozanco, Yolanda E-Martín, Susana Fernández +1

In competitive environments, commonly agents try to prevent opponents from achieving their goals. Most previous preventing approaches assume the opponent's goal is known a priori.…

cs.CL2021

Advising Agent for Service-Providing Live-Chat Operators

Aviram Aviv, Yaniv Oshrat, Samuel A. Assefa +4

Call centers, in which human operators attend clients using textual chat, are very common in modern e-commerce. Training enough skilled operators who are able to provide good servi…

cs.AI2020★ 1 cited

Domain-independent generation and classification of behavior traces

Daniel Borrajo, Manuela Veloso

Financial institutions mostly deal with people. Therefore, characterizing different kinds of human behavior can greatly help institutions for improving their relation with customer…

cs.AI2020★ 1 cited

Goal recognition via model-based and model-free techniques

Daniel Borrajo, Sriram Gopalakrishnan, Vamsi K. Potluru

Goal recognition aims at predicting human intentions from a trace of observations. This ability allows people or organizations to anticipate future actions and intervene in a posit…

cs.AI2020★ 2 cited

Simulating and classifying behavior in adversarial environments based on action-state traces: an application to money laundering

Daniel Borrajo, Manuela Veloso, Sameena Shah

Many business applications involve adversarial relationships in which both sides adapt their strategies to optimize their opposing benefits. One of the key characteristics of these…

cs.AI2019★ 1 cited

Guarantees for Sound Abstractions for Generalized Planning (Extended Paper)

Blai Bonet, Raquel Fuentetaja, Yolanda E-Martin +1

Generalized planning is about finding plans that solve collections of planning instances, often infinite collections, rather than single instances. Recently it has been shown how t…

cs.AI2019★ 1 cited

Error Analysis and Correction for Weighted A*'s Suboptimality (Extended Version)

Robert C. Holte, Ruben Majadas, Alberto Pozanco +1

Weighted A* (wA*) is a widely used algorithm for rapidly, but suboptimally, solving planning and search problems. The cost of the solution it produces is guaranteed to be at most W…

cs.AI2014★ 30 cited

Scaling up Heuristic Planning with Relational Decision Trees

Tomas De la Rosa, Sergio Jimenez, Raquel Fuentetaja +1

Current evaluation functions for heuristic planning are expensive to compute. In numerous planning problems these functions provide good guidance to the solution, so they are worth…