Cooperative solution of constraint satisfaction problems

Recent Progress of Surface Modified Nanomaterials for Scavenging Reactive Oxygen Species in Organism

Reactive oxygen species (ROS) are essential for normal physiological processes and play important roles in signal transduction, immunity, and tissue homeostasis. However, excess ROS may have a negative effect on the normal cells leading to various diseases. Nanomaterials are an attractive therapeutic alternative of antioxidants and possess an intrinsic ability to scavenge ROS. Surface modification for nanomaterials is a critical strategy to improve their comprehensive performances. Herein, we review the different surface modified strategies for nanomaterials to scavenge ROS and their inherent antioxidant capability, mechanisms of action, and biological applications. At last, the primary challenges and future perspectives in this emerging research frontier have also been highlighted. It is believed that this review paper will offer a top understanding and guidance on engineering future high-performance surface modified ROS scavenging nanomaterials for wide biomedical applications.

The influences of social value orientation and domain knowledge on crowdsourcing manuscript transcription

Purpose

The purpose of this paper is to explore how social value orientation and domain knowledge affect cooperation levels and transcription quality in crowdsourced manuscript transcription, and contribute to the recruitment of participants in such projects in practice.

Design/methodology/approach

The authors conducted a quasi-experiment using Transcribe-Sheng, which is a well-known crowdsourced manuscript transcription project in China, to investigate the influences of social value orientation and domain knowledge. The experiment lasted one month and involved 60 participants. ANOVA was used to test the research hypotheses. Moreover, inverviews and thematic analyses were conducted to analyze the qualitative data in order to provide additional insights.

Findings

The analysis confirmed that in crowdsourced manuscript transcription, social value orientation has a significant effect on participants’ cooperation level and transcription quality; domain knowledge has a significant effect on participants’ transcription quality, but not on their cooperation level. The results also reveal the interactive effect of social value orientation and domain knowledge on cooperation levels and quality of transcription. The analysis of the qualitative data illustrated the influences of social value orientation and domain knowledge on crowdsourced manuscript transcription in detail.

Originality/value

Researchers have paid little attention to the impacts of the psychological and cognitive factors on crowdsourced manuscript transcription. This study investigated the effect of social value orientation and the combined effect of social value orientation and domain knowledge in this context. The findings shed light on crowdsourcing transcription initiatives in the cultural heritage domain and can be used to facilitate participant selection in such projects.

Agent-based models of collective intelligence

Collective or group intelligence is manifested in the fact that a team of cooperating agents can solve problems more efficiently than when those agents work in isolation. Although cooperation is, in general, a successful problem solving strategy, it is not clear whether it merely speeds up the time to find the solution, or whether it alters qualitatively the statistical signature of the search for the solution. Here we review and offer insights on two agent-based models of distributed cooperative problem-solving systems, whose task is to solve a cryptarithmetic puzzle. The first model is the imitative learning search in which the agents exchange information on the quality of their partial solutions to the puzzle and imitate the most successful agent in the group. This scenario predicts a very poor performance in the case imitation is too frequent or the group is too large, a phenomenon akin to Groupthink of social psychology. The second model is the blackboard organization in which agents read and post hints on a public blackboard. This brainstorming scenario performs the best when there is a stringent limit to the amount of information that is exhibited on the board. Both cooperative scenarios produce a substantial speed up of the time to solve the puzzle as compared with the situation where the agents work in isolation. The statistical signature of the search, however, is the same as that of the independent search.

Impact of centrality on cooperative processes

The solution of today's complex problems requires the grouping of task forces whose members are usually connected remotely over long physical distances and different time zones. Hence, understanding the effects of imposed communication patterns (i.e., who can communicate with whom) on group performance is important. Here we use an agent-based model to explore the influence of the betweenness centrality of the nodes on the time the group requires to find the global maxima of NK-fitness landscapes. The agents cooperate by broadcasting messages, informing on their fitness to their neighbors, and use this information to copy the more successful agents in their neighborhood. We find that for easy tasks (smooth landscapes), the topology of the communication network has no effect on the performance of the group, and that the more central nodes are the most likely to find the global maximum first. For difficult tasks (rugged landscapes), however, we find a positive correlation between the variance of the betweenness among the network nodes and the group performance. For these tasks, the performances of individual nodes are strongly influenced by the agents' dispositions to cooperate and by the particular realizations of the rugged landscapes.

Imitative learning as a connector of collective brains

The notion that cooperation can aid a group of agents to solve problems more efficiently than if those agents worked in isolation is prevalent in computer science and business circles. Here we consider a primordial form of cooperation – imitative learning – that allows an effective exchange of information between agents, which are viewed as the processing units of a social intelligence system or collective brain. In particular, we use agent-based simulations to study the performance of a group of agents in solving a cryptarithmetic problem. An agent can either perform local random moves to explore the solution space of the problem or imitate a model agent – the best performing agent in its influence network. There is a trade-off between the number of agents and the imitation probability , and for the optimal balance between these parameters we observe a thirtyfold diminution in the computational cost to find the solution of the cryptarithmetic problem as compared with the independent search. If those parameters are chosen far from the optimal setting, however, then imitative learning can impair greatly the performance of the group.

Social interaction as a heuristic for combinatorial optimization problems

We investigate the performance of a variant of Axelrod's model for dissemination of culture--the Adaptive Culture Heuristic (ACH)--on solving an NP-Complete optimization problem, namely, the classification of binary input patterns of size F by a Boolean Binary Perceptron. In this heuristic, N agents, characterized by binary strings of length F which represent possible solutions to the optimization problem, are fixed at the sites of a square lattice and interact with their nearest neighbors only. The interactions are such that the agents' strings (or cultures) become more similar to the low-cost strings of their neighbors resulting in the dissemination of these strings across the lattice. Eventually the dynamics freezes into a homogeneous absorbing configuration in which all agents exhibit identical solutions to the optimization problem. We find through extensive simulations that the probability of finding the optimal solution is a function of the reduced variable F/N(¼) so that the number of agents must increase with the fourth power of the problem size, N∝F⁴, to guarantee a fixed probability of success. In this case, we find that the relaxation time to reach an absorbing configuration scales with F⁶ which can be interpreted as the overall computational cost of the ACH to find an optimal set of weights for a Boolean binary perceptron, given a fixed probability of success.

Commitments and conventions: The foundation of coordination in multi-agent systems

Distributed Artificial Intelligence systems, in which multiple agents interact to improve their individual performance and to enhance the systems' overall utility, are becoming an increasingly pervasive means of conceptualising a diverse range of applications. As the discipline matures, researchers are beginning to strive for the underlying theories and principles which guide the central processes of coordination and cooperation. Here agent communities are modelled using a distributed goal search formalism, and it is argued thatcommitments(pledges to undertake a specific course of action) andconventions(means of monitoring commitments in changing circumstances) are the foundation of coordination in multi-agent systems. An analysis of existing coordination models which use concepts akin to commitments and conventions is undertaken before a new unifying framework is presented. Finally, a number of prominent coordination techniques which do notexplicitlyinvolve commitments or conventions are reformulated in these terms to demonstrate their compliance with the central hypothesis of this paper.

Synthesis of silver nanoparticles for remote opening of polyelectrolyte microcapsules

Silver nanoparticles of 10, 18, and 23 nm were synthesized in aqueous medium by chemical reduction of silver nitrate in excess of sodium borohydride. Modification of polyelectrolyte shells with synthesized silver nanoparticles was performed using the layer-by-layer approach. Remote opening of the polyelectrolyte/silver capsules was performed with a CW Nd:YAG FD laser with an average incident power output up to 70 mW. Capsules with a mixture of 10 and 18 nm silver nanoparticles in its polyelectrolyte shell were ruptured after less than 7 s of laser irradiation, while microcapsules with 23 nm silver nanoparticles in the shell were broken after 11 s of laser treatment and 10 nm silver nanoparticles were broken after 26 s.

Groups of diverse problem solvers can outperform groups of high-ability problem solvers

We introduce a general framework for modeling functionally diverse problem-solving agents. In this framework, problem-solving agents possess representations of problems and algorithms that they use to locate solutions. We use this framework to establish a result relevant to group composition. We find that when selecting a problem-solving team from a diverse population of intelligent agents, a team of randomly selected agents outperforms a team comprised of the best-performing agents. This result relies on the intuition that, as the initial pool of problem solvers becomes large, the best-performing agents necessarily become similar in the space of problem solvers. Their relatively greater ability is more than offset by their lack of problem-solving diversity.

Modelling the evolution of human trail systems

Many human social phenomena, such as cooperation, the growth of settlements, traffic dynamics and pedestrian movement, appear to be accessible to mathematical descriptions that invoke self-organization. Here we develop a model of pedestrian motion to explore the evolution of trails in urban green spaces such as parks. Our aim is to address such questions as what the topological structures of these trail systems are, and whether optimal path systems can be predicted for urban planning. We use an 'active walker' model that takes into account pedestrian motion and orientation and the concomitant feedbacks with the surrounding environment. Such models have previously been applied to the study of complex structure formation in physical, chemical and biological systems. We find that our model is able to reproduce many of the observed large-scale spatial features of trail systems.

An Economics Approach to Hard Computational Problems

A general method for combining existing algorithms into new programs that are unequivocally preferable to any of the component algorithms is presented. This method, based on notions of risk in economics, offers a computational portfolio design procedure that can be used for a wide range of problems. Tested by solving a canonical NP-complete problem, the method can be used for problems ranging from the combinatorics of DNA sequencing to the completion of tasks in environments with resource contention, such as the World Wide Web.

Genetic algorithms for protein structure prediction

Genetic algorithms are a general class of search methods that mimic natural gene-based optimization mechanisms. Mutation, cross-over and replication operations are performed on strings. When applied to structure prediction, each string describes a particular conformation of a protein molecule. There are many ways in which such search methods may be implemented. Recent results show potential for helping with protein structure prediction, but more data are needed before a complete assessment can be made.

Critical Behavior in the Satisfiability of Random Boolean Expressions

Determining the satisfiability of randomly generated Boolean expressions with k variables per clause is a popular test for the performance of search algorithms in artificial intelligence and computer science. It is known that for k = 2, formulas are almost always satisfiable when the ratio of clauses to variables is less than 1; for ratios larger than 1, the formulas are almost never satisfiable. Similar sharp threshold behavior is observed for higher values of k. Finite-size scaling, a method from statistical physics, can be used to characterize size-dependent effects near the threshold. A relationship can be drawn between thresholds and computational complexity.