Grounding of Meaning in Sensori-Motor Process

There is an increasing agreement in the cognitive sciences community that our sensations are closely related to our actions. Our actions impact our sensations from the environment and the knowledge we have of it. Cognition is grounded in sensori-motor coordination. \ud In the perspective of implementing such a performance in artificial systems, there is a need for a model of sensori-motor coordination.\ud We propose here such a model as based on the generation of meaningful information by a system submitted to a constraint [1]. Systems and agents have constraints to satisfy which are related to their nature (stay alive for an organism, avoid obstacle for a robot, …). We propose here to use an existing meaning generation process where a system submitted to a constraint generates a meaningful information (a meaning) when it receives an information that has a connection with the constraint [2]. The generated meaning is precisely the connection existing between the received information and the constraint of the system. The generated meaning is used to trigger an action that will satisfy the constraint. The generated meaning links the system to its environment. A Meaning Generator System (MGS) has been introduced as a building block for higher level systems (agents). The MGS allows to link sensation and action through the satisfaction of the constraint of the system/agent. We use the MGS in a model which is based on constraint satisfaction for sensori-motor coordination in agents, be they organic or artificial. The meaning is generated by and for the agent that hosts the MGS. Such approach makes possible an addressing of the concept of autonomy through the intrinsic or artificial nature of the constraint to be satisfied (organisms with intrinsic constraints/autonomy, artificial systems with artificial constraints/autonomy). The systemic nature of the MGS also allows to position the groundings of the generated meaning as being in or out of the MGS, and correspondingly identify the constructivist and objectivist components of the generated meaning. \ud The approach presented here makes available a sensori-motor coordination by meaning generation through constraint satisfaction with groundings of the generated meaning.\u

Position-Based Multi-Agent Dynamics for Real-Time Crowd Simulation (MiG paper)

Exploiting the efficiency and stability of Position-Based Dynamics (PBD), we introduce a novel crowd simulation method that runs at interactive rates for hundreds of thousands of agents. Our method enables the detailed modeling of per-agent behavior in a Lagrangian formulation. We model short-range and long-range collision avoidance to simulate both sparse and dense crowds. On the particles representing agents, we formulate a set of positional constraints that can be readily integrated into a standard PBD solver. We augment the tentative particle motions with planning velocities to determine the preferred velocities of agents, and project the positions onto the constraint manifold to eliminate colliding configurations. The local short-range interaction is represented with collision and frictional contact between agents, as in the discrete simulation of granular materials. We incorporate a cohesion model for modeling collective behaviors and propose a new constraint for dealing with potential future collisions. Our new method is suitable for use in interactive games.Comment: 9 page

Building a Truly Distributed Constraint Solver with JADE

Real life problems such as scheduling meeting between people at different locations can be modelled as distributed Constraint Satisfaction Problems (CSPs). Suitable and satisfactory solutions can then be found using constraint satisfaction algorithms which can be exhaustive (backtracking) or otherwise (local search). However, most research in this area tested their algorithms by simulation on a single PC with a single program entry point. The main contribution of our work is the design and implementation of a truly distributed constraint solver based on a local search algorithm using Java Agent DEvelopment framework (JADE) to enable communication between agents on different machines. Particularly, we discuss design and implementation issues related to truly distributed constraint solver which might not be critical when simulated on a single machine. Evaluation results indicate that our truly distributed constraint solver works well within the observed limitations when tested with various distributed CSPs. Our application can also incorporate any constraint solving algorithm with little modifications.Comment: 7 page

A Duality-Based Approach for Distributed Optimization with Coupling Constraints

In this paper we consider a distributed optimization scenario in which a set of agents has to solve a convex optimization problem with separable cost function, local constraint sets and a coupling inequality constraint. We propose a novel distributed algorithm based on a relaxation of the primal problem and an elegant exploration of duality theory. Despite its complex derivation based on several duality steps, the distributed algorithm has a very simple and intuitive structure. That is, each node solves a local version of the original problem relaxation, and updates suitable dual variables. We prove the algorithm correctness and show its effectiveness via numerical computations

Ontology-based specific and exhaustive user profiles for constraint information fusion for multi-agents

Intelligent agents are an advanced technology utilized in Web Intelligence. When searching information from a distributed Web environment, information is retrieved by multi-agents on the client site and fused on the broker site. The current information fusion techniques rely on cooperation of agents to provide statistics. Such techniques are computationally expensive and unrealistic in the real world. In this paper, we introduce a model that uses a world ontology constructed from the Dewey Decimal Classification to acquire user profiles. By search using specific and exhaustive user profiles, information fusion techniques no longer rely on the statistics provided by agents. The model has been successfully evaluated using the large INEX data set simulating the distributed Web environment