Experimental Study and Modeling of Three Classes of Collective Problem-Solving Methods

People working together can be very successful problem-solvers. Many real-life examples, from Wikipedia to citizen science projects, show that, under the right conditions, crowds can find remarkable solutions to complex problems. Yet, joining the capabilities of many people can be challenging. What factors make some groups more successful than others? How does the nature of the problem and the structure of the environment influence the group's performance? To answer these questions, I consider problem-solving as a search process -- a situation in which individuals are searching for a good solution. I describe and compare three different methods for structuring groups: (1) non-interacting groups, where individuals search independently without exchanging any information, (2) social groups, where individuals freely exchange information during their search, and (3) solution-influenced groups, where individuals repeatedly contribute to a shared collective solution. First, I introduce the idea of transmission chains - a specific type of solution-influenced group where individuals tackle the problem one after another, each one starting from the solution of its predecessor. I apply this method to binary choice problems and compare it to majority voting rules in non-interacting groups. The results show that transmission chains are superior in environments where individual accuracy is low and confidence is a reliable indicator of performance. This type of environment, however, is rarely observed in two experimental datasets. Then, I evaluate the performance of transmission chains for problems that have a complex structure, such as multidimensional optimization tasks. Again, I use non-interacting groups as a comparison, this time by selecting the best out of multiple independent solutions. Simulations and experimental data show that transmission chains outperform independent groups under two environmental conditions: either when problems are rather easy, or when group members are relatively unskilled. Next, I focus on social groups, where individuals influence each other during the search. To understand the social dynamics that operate in such groups, I conduct two studies: I first examine how people search for a solution independently from others, and then study how this individual process is impacted by social influence. The first study presents experimental data to show that the individual search behavior can be described by a take-the-best heuristic, that is, a simple rule-of-thumb that ignores all but one cue at a time. This heuristic reproduces a variety of behavioral patterns observed in different environments. Then, I extend this heuristic to include social interactions where multiple individuals exchange information during their search. My results show that, in this case, individuals tend to converge towards similar solutions. This induces a collective search dilemma: compared to non-interacting groups, the quality of the average individual's solution is improved at the expense of the best solution of the group. Nevertheless, further analyses show that this dilemma disappears for more difficult problems. Overall, this thesis shows that no collective problem-solving method is superior to the others in all environments and for all problems. Instead, the performance of each method depends on numerous factors, such as the nature of the task, the problem difficulty, the group composition, and the skill levels of the individuals. My work helps understanding the role of these different factors and their influence on collective problem-solving

Computational aspects of cellular intelligence and their role in artificial intelligence.

The work presented in this thesis is concerned with an exploration of the computational aspects of the primitive intelligence associated with single-celled organisms. The main aim is to explore this Cellular Intelligence and its role within Artificial Intelligence. The findings of an extensive literature search into the biological characteristics, properties and mechanisms associated with Cellular Intelligence, its underlying machinery - Cell Signalling Networks and the existing computational methods used to capture it are reported. The results of this search are then used to fashion the development of a versatile new connectionist representation, termed the Artificial Reaction Network (ARN). The ARN belongs to the branch of Artificial Life known as Artificial Chemistry and has properties in common with both Artificial Intelligence and Systems Biology techniques, including: Artificial Neural Networks, Artificial Biochemical Networks, Gene Regulatory Networks, Random Boolean Networks, Petri Nets, and S-Systems. The thesis outlines the following original work: The ARN is used to model the chemotaxis pathway of Escherichia coli and is shown to capture emergent characteristics associated with this organism and Cellular Intelligence more generally. The computational properties of the ARN and its applications in robotic control are explored by combining functional motifs found in biochemical network to create temporal changing waveforms which control the gaits of limbed robots. This system is then extended into a complete control system by combining pattern recognition with limb control in a single ARN. The results show that the ARN can offer increased flexibility over existing methods. Multiple distributed cell-like ARN based agents termed Cytobots are created. These are first used to simulate aggregating cells based on the slime mould Dictyostelium discoideum. The Cytobots are shown to capture emergent behaviour arising from multiple stigmergic interactions. Applications of Cytobots within swarm robotics are investigated by applying them to benchmark search problems and to the task of cleaning up a simulated oil spill. The results are compared to those of established optimization algorithms using similar cell inspired strategies, and to other robotic agent strategies. Consideration is given to the advantages and disadvantages of the technique and suggestions are made for future work in the area. The report concludes that the Artificial Reaction Network is a versatile and powerful technique which has application in both simulation of chemical systems, and in robotic control, where it can offer a higher degree of flexibility and computational efficiency than benchmark alternatives. Furthermore, it provides a tool which may possibly throw further light on the origins and limitations of the primitive intelligence associated with cells

Complex Adaptive Systems & Urban Morphogenesis:

This thesis looks at how cities operate as Complex Adaptive Systems (CAS). It focuses on how certain characteristics of urban form can support an urban environment's capacity to self-organize, enabling emergent features to appear that, while unplanned, remain highly functional. The research is predicated on the notion that CAS processes operate across diverse domains: that they are ‘generalized' or ‘universal'. The goal of the dissertation is then to determine how such generalized principles might ‘play out' within the urban fabric. The main thrust of the work is to unpack how elements of the urban fabric might be considered as elements of a complex system and then identify how one might design these elements in a more deliberate manner, such that they hold a greater embedded capacity to respond to changing urban forces. The research is further predicated on the notion that, while such responses are both imbricated with, and stewarded by human actors, the specificities of the material characteristics themselves matter. Some forms of material environments hold greater intrinsic physical capacities (or affordances) to enact the kinds of dynamic processes observed in complex systems than others (and can, therefore, be designed with these affordances in mind). The primary research question is thus:   What physical and morphological conditions need to be in place within an urban environment in order for Complex Adaptive Systems dynamics arise - such that the physical components (or ‘building blocks') of the urban environment have an enhanced capacity to discover functional configurations in space and time as a response to unfolding contextual conditions?   To answer this question, the dissertation unfolds in a series of parts. It begins by attempting to distill the fundamental dynamics of a Complex Adaptive System. It does so by means of an extensive literature review that examines a variety of highly cited ‘defining principles' or ‘key attributes' of CAS. These are cross-referenced so as to extract common features and distilled down into six major principles that are considered as the generalized features of any complex system, regardless of domain. In addition, this section considers previous urban research that engages complexity principles in order to better position the distinctive perspective of this thesis. This rests primarily on the dissertation's focus on complex urban processes that occur by means of materially enabled in situ processes. Such processes have, it is argued, remained largely under-theorized. The opening section presents introductory examples of what might be meant by a ‘materially enabling' environment.   The core section of the research then undertakes a more detailed unpacking of how complex processes can be understood as having a morphological dimension. This section begins by discussing, in broad terms, the potential ‘phase space' of a physical environment and how this can be expanded or limited according to a variety of factors. Drawing insights from related inquiries in the field of Evolutionary Economic Geography, the research argues that, while emergent capacity is often explored in social, economic, or political terms, it is under-theorized in terms of the concrete physical sub-strata that can also act to ‘carry' or ‘moor' CAS dynamics. This theme is advanced in the next article, where a general framework for speaking about CAS within urban environments is introduced. This framework borrows from the terms for ‘imageability' that were popularized by Kevin Lynch: paths, edges, districts, landmarks, and nodes. These terms are typically associated with physical or ‘object-like features' of the urban environment – that is to say, their image. The terminology is then co-opted such that it makes reference not simply to physical attributes, but rather to the complex processes these attributes enable. To advance this argument, the article contrasts the static and ‘imageable' qualities of New Urbanism projects with the ‘unfolding' and dynamic qualities of complex systems - critiquing NU proponents as failing to appreciate the underlying forces that generate the environments they wish to emulate. Following this, the efficacy of the re-purposed ‘Lynchian' framework is tested using the case study of Istanbul's Grand Bazaar. Here, specific elements of the Bazaar's urban fabric are positioned as holding material agency that enables particular emergent spatial phenomena to manifest. In addition, comparisons are drawn between physical dynamics unfolding within the Bazaar's morphological setting (leading to emergent merchant districts) and parallel dynamics explored within Evolutionary Economic Geography).   The last section of the research extends this research to consider digitally augmented urban elements that hold an enhanced ability to receive and convey information. A series of speculative thought-experiments highlight how augmented urban entities could employ CAS dynamics to ‘solve for' different kinds of urban optimization scenarios, leading these material entities to self-organize (with their users) and discover fit regimes. The final paper flips the perspective, considering how, not only material agency, but also human agency is being augmented by new information processing technologies (smartphones), and how this can lead to new dances of agency that in turn generate novel emergent outcomes.   The dissertation is based on a compilation of articles that have, for the most part, been published in academic journals and all the research has been presented at peer-reviewed academic conferences. An introduction, conclusion, and explanatory transitions between sections are provided in order to clarify the narrative thread between the sections and the articles. Finally, a brief ‘coda' on the spatial dynamics afforded by Turkish Tea Gardens is offered

Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning

Contains fulltext : 228326pre.pdf (preprint version ) (Open Access) Contains fulltext : 228326pub.pdf (publisher's version ) (Open Access)BNAIC/BeneLearn 202

Agent Based Models of Competition and Collaboration

Swarm intelligence is a popular paradigm for algorithm design. Frequently drawing inspiration from natural systems, it assigns simple rules to a set of agents with the aim that, through local interactions, they collectively solve some global problem. Current variants of a popular swarm based optimization algorithm, particle swarm optimization (PSO), are investigated with a focus on premature convergence. A novel variant, dispersive PSO, is proposed to address this problem and is shown to lead to increased robustness and performance compared to current PSO algorithms. A nature inspired decentralised multi-agent algorithm is proposed to solve a constrained problem of distributed task allocation. Agents must collect and process the mail batches, without global knowledge of their environment or communication between agents. New rules for specialisation are proposed and are shown to exhibit improved eciency and exibility compared to existing ones. These new rules are compared with a market based approach to agent control. The eciency (average number of tasks performed), the exibility (ability to react to changes in the environment), and the sensitivity to load (ability to cope with differing demands) are investigated in both static and dynamic environments. A hybrid algorithm combining both approaches, is shown to exhibit improved eciency and robustness. Evolutionary algorithms are employed, both to optimize parameters and to allow the various rules to evolve and compete. We also observe extinction and speciation. In order to interpret algorithm performance we analyse the causes of eciency loss, derive theoretical upper bounds for the eciency, as well as a complete theoretical description of a non-trivial case, and compare these with the experimental results. Motivated by this work we introduce agent "memory" (the possibility for agents to develop preferences for certain cities) and show that not only does it lead to emergent cooperation between agents, but also to a signicant increase in efficiency

Computing multi-scale organizations built through assembly

The ability to generate and control assembling structures built over many orders of magnitude is an unsolved challenge of engineering and science. Many of the presumed transformational benefits of nanotechnology and robotics are based directly on this capability. There are still significant theoretical difficulties associated with building such systems, though technology is rapidly ensuring that the tools needed are becoming available in chemical, electronic, and robotic domains. In this thesis a simulated, general-purpose computational prototype is developed which is capable of unlimited assembly and controlled by external input, as well as an additional prototype which, in structures, can emulate any other computing device. These devices are entirely finite-state and distributed in operation. Because of these properties and the unique ability to form unlimited size structures of unlimited computational power, the prototypes represent a novel and useful blueprint on which to base scalable assembly in other domains. A new assembling model of Computational Organization and Regulation over Assembly Levels (CORAL) is also introduced, providing the necessary framework for this investigation. The strict constraints of the CORAL model allow only an assembling unit of a single type, distributed control, and ensure that units cannot be reprogrammed - all reprogramming is done via assembly. Multiple units are instead structured into aggregate computational devices using a procedural or developmental approach. Well-defined comparison of computational power between levels of organization is ensured by the structure of the model. By eliminating ambiguity, the CORAL model provides a pragmatic answer to open questions regarding a framework for hierarchical organization. Finally, a comparison between the designed prototypes and units evolved using evolutionary algorithms is presented as a platform for further research into novel scalable assembly. Evolved units are capable of recursive pairing ability under the control of a signal, a primitive form of unlimited assembly, and do so via symmetry-breaking operations at each step. Heuristic evidence for a required minimal threshold of complexity is provided by the results, and challenges and limitations of the approach are identified for future evolutionary studies

Cooperative Particle Swarm Optimization for Combinatorial Problems

A particularly successful line of research for numerical optimization is the well-known computational paradigm particle swarm optimization (PSO). In the PSO framework, candidate solutions are represented as particles that have a position and a velocity in a multidimensional search space. The direct representation of a candidate solution as a point that flies through hyperspace (i.e., Rn) seems to strongly predispose the PSO toward continuous optimization. However, while some attempts have been made towards developing PSO algorithms for combinatorial problems, these techniques usually encode candidate solutions as permutations instead of points in search space and rely on additional local search algorithms. In this dissertation, I present extensions to PSO that by, incorporating a cooperative strategy, allow the PSO to solve combinatorial problems. The central hypothesis is that by allowing a set of particles, rather than one single particle, to represent a candidate solution, combinatorial problems can be solved by collectively constructing solutions. The cooperative strategy partitions the problem into components where each component is optimized by an individual particle. Particles move in continuous space and communicate through a feedback mechanism. This feedback mechanism guides them in the assessment of their individual contribution to the overall solution. Three new PSO-based algorithms are proposed. Shared-space CCPSO and multispace CCPSO provide two new cooperative strategies to split the combinatorial problem, and both models are tested on proven NP-hard problems. Multimodal CCPSO extends these combinatorial PSO algorithms to efficiently sample the search space in problems with multiple global optima. Shared-space CCPSO was evaluated on an abductive problem-solving task: the construction of parsimonious set of independent hypothesis in diagnostic problems with direct causal links between disorders and manifestations. Multi-space CCPSO was used to solve a protein structure prediction subproblem, sidechain packing. Both models are evaluated against the provable optimal solutions and results show that both proposed PSO algorithms are able to find optimal or near-optimal solutions. The exploratory ability of multimodal CCPSO is assessed by evaluating both the quality and diversity of the solutions obtained in a protein sequence design problem, a highly multimodal problem. These results provide evidence that extended PSO algorithms are capable of dealing with combinatorial problems without having to hybridize the PSO with other local search techniques or sacrifice the concept of particles moving throughout a continuous search space

Population-based runtime optimisation in static and dynamic environments

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