Modelling of Multi-Agent Systems: Experiences with Membrane Computing and Future Challenges

Formal modelling of Multi-Agent Systems (MAS) is a challenging task due to high complexity, interaction, parallelism and continuous change of roles and organisation between agents. In this paper we record our research experience on formal modelling of MAS. We review our research throughout the last decade, by describing the problems we have encountered and the decisions we have made towards resolving them and providing solutions. Much of this work involved membrane computing and classes of P Systems, such as Tissue and Population P Systems, targeted to the modelling of MAS whose dynamic structure is a prominent characteristic. More particularly, social insects (such as colonies of ants, bees, etc.), biology inspired swarms and systems with emergent behaviour are indicative examples for which we developed formal MAS models. Here, we aim to review our work and disseminate our findings to fellow researchers who might face similar challenges and, furthermore, to discuss important issues for advancing research on the application of membrane computing in MAS modelling.Comment: In Proceedings AMCA-POP 2010, arXiv:1008.314

An Agent-Based Approach to Self-Organized Production

The chapter describes the modeling of a material handling system with the production of individual units in a scheduled order. The units represent the agents in the model and are transported in the system which is abstracted as a directed graph. Since the hindrances of units on their path to the destination can lead to inefficiencies in the production, the blockages of units are to be reduced. Therefore, the units operate in the system by means of local interactions in the conveying elements and indirect interactions based on a measure of possible hindrances. If most of the units behave cooperatively ("socially"), the blockings in the system are reduced. A simulation based on the model shows the collective behavior of the units in the system. The transport processes in the simulation can be compared with the processes in a real plant, which gives conclusions about the consequencies for the production based on the superordinate planning.Comment: For related work see http://www.soms.ethz.c

Fast and robust learning by reinforcement signals: explorations in the insect brain

We propose a model for pattern recognition in the insect brain. Departing from a well-known body of knowledge about the insect brain, we investigate which of the potentially present features may be useful to learn input patterns rapidly and in a stable manner. The plasticity underlying pattern recognition is situated in the insect mushroom bodies and requires an error signal to associate the stimulus with a proper response. As a proof of concept, we used our model insect brain to classify the well-known MNIST database of handwritten digits, a popular benchmark for classifiers. We show that the structural organization of the insect brain appears to be suitable for both fast learning of new stimuli and reasonable performance in stationary conditions. Furthermore, it is extremely robust to damage to the brain structures involved in sensory processing. Finally, we suggest that spatiotemporal dynamics can improve the level of confidence in a classification decision. The proposed approach allows testing the effect of hypothesized mechanisms rather than speculating on their benefit for system performance or confidence in its responses

Abundance of insects in rice mills in Polonnaruwa, Sri Lanka: Poster

Monitoring of insect population is a prerequisite for integrated pest management attempts. The complex structures/machines in rice milling facilities, however, limit surveying attempts aggravating the ignorance of insect fauna associated with such facilities. Furthermore, insect surveys conducted in Sri Lanka are very rare. The objective of the current study was to determine the presence, diversity, and abundance of insects in rice mills of varying capacity as found in a major rice processing area in Sri Lanka. A group of large-, medium-, and smallscale mills were used for the survey. Samples were collected from different locations in the mills, and the density of insects at each location was determined. Insect species and their abundance varied with the type of mill as well as with the location in the mill. This information is useful to design and implement pest management for the mills.Monitoring of insect population is a prerequisite for integrated pest management attempts. The complex structures/machines in rice milling facilities, however, limit surveying attempts aggravating the ignorance of insect fauna associated with such facilities. Furthermore, insect surveys conducted in Sri Lanka are very rare. The objective of the current study was to determine the presence, diversity, and abundance of insects in rice mills of varying capacity as found in a major rice processing area in Sri Lanka. A group of large-, medium-, and smallscale mills were used for the survey. Samples were collected from different locations in the mills, and the density of insects at each location was determined. Insect species and their abundance varied with the type of mill as well as with the location in the mill. This information is useful to design and implement pest management for the mills

Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation

An originally chaotic system can be controlled into various periodic dynamics. When it is implemented into a legged robot's locomotion control as a central pattern generator (CPG), sophisticated gait patterns arise so that the robot can perform various walking behaviors. However, such a single chaotic CPG controller has difficulties dealing with leg malfunction. Specifically, in the scenarios presented here, its movement permanently deviates from the desired trajectory. To address this problem, we extend the single chaotic CPG to multiple CPGs with learning. The learning mechanism is based on a simulated annealing algorithm. In a normal situation, the CPGs synchronize and their dynamics are identical. With leg malfunction or disability, the CPGs lose synchronization leading to independent dynamics. In this case, the learning mechanism is applied to automatically adjust the remaining legs' oscillation frequencies so that the robot adapts its locomotion to deal with the malfunction. As a consequence, the trajectory produced by the multiple chaotic CPGs resembles the original trajectory far better than the one produced by only a single CPG. The performance of the system is evaluated first in a physical simulation of a quadruped as well as a hexapod robot and finally in a real six-legged walking machine called AMOSII. The experimental results presented here reveal that using multiple CPGs with learning is an effective approach for adaptive locomotion generation where, for instance, different body parts have to perform independent movements for malfunction compensation.Comment: 48 pages, 16 figures, Information Sciences 201

ICROFS news 2/2013. Newsletter from ICROFS

Productivity and Growth in Organic Value-chains (ProGrOV): Networks organization along organic foods value chains in Kenya. Case: Kales in Nairo - SUMMER chickens ”on herbs” - Spotless apples under roof - Organic hay fields as a floral resource for bees and other flower-visiting insects - Larvae for layer

D2_2. Status Quo Analysis all Countries

STATUS QUO ANALYSIS OF APPLE ORGANIC FARMING IN THE DIFFERENT COUNTRIE

How to stop "weevil" damage in stored grain

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