Specializing Russian Doll Search

7th International Conference, CP 2001, Paphos, Cyprus, November 26 - December 1, 2001.Russian Doll Search (RDS) is a clever procedure to solve overconstrained problems. RDS solves a sequence of nested subproblems, where two consecutive subproblems differ in one variable only. We present the Specialized RDS (SRDS) algorithm, which solves the current subproblem for each value of the new variable with respect to the previous subproblem. The SRDS lower bound is superior to the RDS lower bound, which allows for a higher level of value pruning, although more work per node is required. Experimental results on random and real problems show that this extra work is often beneficial, providing substantial savings in the global computational effort.This work was supported by the IST Programme of the Commission of the European Union through the ECSPLAIN project (IST-1999-11969), and by the Spanish CICYT project TAP99-1086-C03-02

Pseudo-Tree Search with Soft Constraints

Trabajo presentado en la 15th European Conference on Artificial Intelligence (ECAI'02), Lyon France July 21 - 26, 2002.Pseudo-tree search is a well known algorithm for CSP solving. It exploits the problem structure to detect independent subproblems that are solved separately. Its main advantage is that its run time complexity is bounded by a problem structural parameter. In this paper, we extend this idea to soft constraint problems. We show that the same general principles apply to this domain. However, a naive implementation is not competitive with state-of-the-art algorithms, because solving independent problems separately may yield a poor algorithmic efficiency due to loose upper bounds. We introduce PT-BB, a branch-and-bound algorithm that performs efficient pseudo-tree search. Its main feature is the use of local upper bounds which can improve over loose global upper bounds. We also show that PT-BB combines nicely with russian doll search (RDS), producing an interesting algorithm.Peer reviewe

Cooperative control of environmental extremes by artificial intelligent agents

Humans have been able to tackle biosphere complexities by acting as ecosystem engineers, profoundly changing the flows of matter, energy and information. This includes major innovations that allowed to reduce and control the impact of extreme events. Modelling the evolution of such adaptive dynamics can be challenging given the potentially large number of individual and environmental variables involved. This paper shows how to address this problem by using fire as the source of external, bursting and wide fluctuations. Fire propagates on a spatial landscape where a group of agents harvest and exploit trees while avoiding the damaging effects of fire spreading. The agents need to solve a conflict to reach a group-level optimal state: while tree harvesting reduces the propagation of fires, it also reduces the availability of resources provided by trees. It is shown that the system displays two major evolutionary innovations that end up in an ecological engineering strategy that favours high biomass along with the suppression of large fires. The implications for potential A.I. management of complex ecosystems are discussed

Autonomous development of turn-taking behaviors in agent populations: a computational study

Comunicació presentada a 5th IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob); 2015 Aug 13-16; Providence, USA.We provide an original computational model showing how turn-taking behaviors can self-organize out of sensorimotor/ninteractions between vocalizing agents. These agents are equipped with a cognitive architecture based on two coupled/ncontrol loops: a reactive one implementing a basic regulatory behavior to maintain vocal listening and an adaptive one learning an action policy to maximize an overall group presence estimation. We show that the reactive process allows to bootstrap the adaptive learning to converge toward a collective turn-taking strategy. This model provides a computational support to the hypothesis that turn-taking can emerge from functional constraints related to group cohesion and vocal signal interferences and suggests future directions of research to understand how social behaviors/ncan result from sensorimotor interactions.This work is supported by the Socialising Sensori-Motor Contingencies project socSMC-641321H2020-FETPROACT-2014

Autonomous development of turn-taking behaviors in agent populations: a computational study

Comunicació presentada a 5th IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob); 2015 Aug 13-16; Providence, USA.We provide an original computational model showing how turn-taking behaviors can self-organize out of sensorimotor/ninteractions between vocalizing agents. These agents are equipped with a cognitive architecture based on two coupled/ncontrol loops: a reactive one implementing a basic regulatory behavior to maintain vocal listening and an adaptive one learning an action policy to maximize an overall group presence estimation. We show that the reactive process allows to bootstrap the adaptive learning to converge toward a collective turn-taking strategy. This model provides a computational support to the hypothesis that turn-taking can emerge from functional constraints related to group cohesion and vocal signal interferences and suggests future directions of research to understand how social behaviors/ncan result from sensorimotor interactions.This work is supported by the Socialising Sensori-Motor Contingencies project socSMC-641321H2020-FETPROACT-2014

Autonomous development of turn-taking behaviors in agent populations: a computational study

Comunicació presentada a 5th IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob); 2015 Aug 13-16; Providence, USA.We provide an original computational model showing how turn-taking behaviors can self-organize out of sensorimotor/ninteractions between vocalizing agents. These agents are equipped with a cognitive architecture based on two coupled/ncontrol loops: a reactive one implementing a basic regulatory behavior to maintain vocal listening and an adaptive one learning an action policy to maximize an overall group presence estimation. We show that the reactive process allows to bootstrap the adaptive learning to converge toward a collective turn-taking strategy. This model provides a computational support to the hypothesis that turn-taking can emerge from functional constraints related to group cohesion and vocal signal interferences and suggests future directions of research to understand how social behaviors/ncan result from sensorimotor interactions.This work is supported by the Socialising Sensori-Motor Contingencies project socSMC-641321H2020-FETPROACT-2014