Arrow Research search

Author name cluster

Fran

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

13 papers
1 author row

Possible papers

13

IJCAI Conference 2016 Conference Paper

A Tool for Generating Interactive Euler Diagrams

  • Fran
  • ccedil; ois Schwarzentruber

We describe a tool for generating Euler diagrams from a set of region connection calculus formulas. The generation is based on a variant of local search capturing default reasoning for improving aesthetic appearance of Euler diagrams. We also describe an optimization for diagrams to be interactive: the user can modify the diagram with the mouse while formulas are still satisfied. We also discuss how such a tool may propose new relevant formulas to add to the specification using an approximation algorithm based on the satisfiability of Horn clauses.

PDF Details

IJCAI Conference 2016 Conference Paper

Query-Driven Repairing of Inconsistent DL-Lite Knowledge Bases

  • Meghyn Bienvenu
  • Camille Bourgaux
  • Fran
  • ccedil; ois Goasdou
  • eacute;

We consider the problem of query-driven repairing of inconsistent DL-Lite knowledge bases: query answers are computed under inconsistency-tolerant semantics, and the user provides feedback about which answers are erroneous or missing. The aim is to find a set of ABox modifications (deletions and additions), called a repair plan, that addresses as many of the defects as possible. After formalizing this problem and introducing different notions of optimality, we investigate the computational complexity of reasoning about optimal repair plans and propose interactive algorithms for computing such plans. For deletion-only repair plans, we also present a prototype implementation of the core components of the algorithm.

PDF Details

IJCAI Conference 2015 Conference Paper

Exploiting Separability in Multiagent Planning with Continuous-State MDPs (Extended Abstract)

  • Jilles Steeve Dibangoye
  • Christopher Amato
  • Olivier Buffet
  • Fran
  • ccedil; ois Charpillet

Decentralized partially observable Markov decision processes (Dec-POMDPs) provide a general model for decision-making under uncertainty in cooperative decentralized settings, but are difficult to solve optimally (NEXP-Complete). As a new way of solving these problems, we recently introduced a method for transforming a Dec-POMDP into a continuous-state deterministic MDP with a piecewise-linear and convex value function. This new Dec-POMDP formulation, which we call an occupancy MDP, allows powerful POMDP and continuous-state MDP methods to be used for the first time. However, scalability remains limited when the number of agents or problem variables becomes large. In this paper, we show that, under certain separability conditions of the optimal value function, the scalability of this approach can increase considerably. This separability is present when there is locality of interaction between agents, which can be exploited to improve performance. Unlike most previous methods, the novel continuous-state MDP algorithm retains optimality and convergence guarantees. Results show that the extension using separability can scale to a large number of agents and domain variables while maintaining optimality.

PDF Details

IJCAI Conference 2015 Conference Paper

Generating all Possible Palindromes from Ngram Corpora

  • Alexandre Papadopoulos
  • Pierre Roy
  • Jean-Charles R
  • eacute; gin
  • Fran
  • ccedil; ois Pachet

We address the problem of generating all possible palindromes from a corpus of Ngrams. Palindromes are texts that read the same both ways. Short palindromes (“race car”) usually carry precise, significant meanings. Long palindromes are often less meaningful, but even harder to generate. The palindrome generation problem has never been addressed, to our knowledge, from a strictly combinatorial point of view. The main difficulty is that generating palindromes require the simultaneous consideration of two inter-related levels in a sequence: the “character” and the “word” levels. Although the problem seems very combinatorial, we propose an elegant yet non-trivial graph structure that can be used to generate all possible palindromes from a given corpus of Ngrams, with a linear complexity. We illustrate our approach with short and long palindromes obtained from the Google Ngram corpus. We show how we can control the semantics, to some extent, by using arbitrary text corpora to bias the probabilities of certain sets of words. More generally this work addresses the issue of modelling human virtuosity from a combinatorial viewpoint, as a means to understand human creativity.

PDF Details

IJCAI Conference 2015 Conference Paper

Max Order: A Tale of Creativity

  • Fiammetta Ghedini
  • Fran
  • ccedil; ois Pachet
  • Pierre Roy

We present a graphic novel project aiming at illustrating current research results and issues regarding the creative process and its relation with artificial intelligence. The main character, Max Order, is an artist who symbolizes the difficulty of coming up with new, creative ideas, giving up imitation of others and finding one’s own style.

PDF Details

IJCAI Conference 2011 Conference Paper

Finite-Length Markov Processes with Constraints

  • Fran
  • ccedil; ois Pachet
  • Pierre Roy
  • Gabriele Barbieri

Many systems use Markov models to generate finite-length sequences that imitate a given style. These systems often need to enforce specific control constraints on the sequences to generate. Unfortunately, control constraints are not compatible with Markov models, as they induce long-range dependencies that violate the Markov hypothesis of limited memory. Attempts to solve this issue using heuristic search do not give any guarantee on the nature and probability of the sequences generated. We propose a novel and efficient approach to controlled Markov generation for a specific class of control constraints that 1) guarantees that generated sequences satisfy control constraints and 2) follow the statistical distribution of the initial Markov model. Revisiting Markov generation in the framework of constraint satisfaction, we show how constraints can be compiled into a non-homogeneous Markov model, using arc-consistency techniques and renormalization. We illustrate the approach on a melody generation problem and sketch some realtime applications in which control constraints are given by gesture controllers.

PDF Details DOI

AAMAS Conference 2010 Conference Paper

Influence of Different Execution Models on Patrolling Ants Behaviors: from Agents to Robots

  • Arnaud Glad
  • Olivier Simonin
  • Olivier Buffet
  • Fran
  • ccedil; ois Charpillet

Generally, swarm models and algorithms consider synchronous agents, i. e. , they act simultaneously. This hypothesisdoes not fit multi-agent simulators nor robotic systems. Inthis paper, we consider such issues on a patrolling ant algorithm. We examine how different execution hypothesesinfluence self-organization capabilities and patrolling performances of this algorithm. We consider the mono and multiagent cases, the synchronism and determinism hypotheses, and the execution of the model with real robots.

PDF

AAMAS Conference 2008 Conference Paper

Trackside DEIRA: A Dynamic Engaging Intelligent Reporter Agent

  • Fran
  • ccedil; ois Knoppel
  • Almer Tigelaar
  • Danny Oude Bos
  • Thijs Alofs
  • Zsofi Ruttkay

DEIRA is a virtual agent commenting on virtual horse races in real time. DEIRA analyses the state of the race, acts emotionally and comments about the situation in a believable and engaging way, using synthesized speech and facial expressions. In this paper we discuss the challenges, explain the computational models for the cognitive, emotional and communicative behavior, and account on implementation and feedback from users.

PDF
v2026.09.13