DEVELOPMENT OF A MATHEMATICAL MODEL FOR ASSESSING THE EFFICIENCY OF DETECTION AND RECOGNITION OF OBJECTS BY UAVS

The article describes a case study methodology that is applied in RAFAEL for the verification and validation (V&V) of complex and multi-disciplinary systems. Various methods of V&V are found within the nature and the type of the product. Some applications use methods that are prevalent in the electronics industry. There are other methods that are based on international standards such as V&V for airborne structures. Complex systems are characterized by a number of special issues which do not allow for a simple implementation of V&V mentioned above. The following issues are unique to complex systems: the design consists of multi-disciplinary subjects, the cost of the life cycle is high, it takes a long time for hardware production and for the completion of development, there is a demand for high reliability, the V&V process contains a multiplicity of parameters and the system has multiple interfaces. For systems of this nature there is no V&V process available in use and it is necessary to implement a tailored-made method. This method of V&V deals with the two main quantitative and qualitative questions of proof: (a) how does the system and sub-system behave under external environmental conditions?, (b) how does the system and sub-system functioning under the existence of differences between sub-systems and components which are supplied in the delivery stage of the life cycle (i.e. geometrical and performance tolerances, time depending parameters)? The new approach is to design a process of V&V in the early stages of the product life cycle. It is different from the conventional approach which performs the reliability tests at the completion of the product development via the approval examinations. The steps in building updated V&V process for complex system are: 1. Identification of the functionality specification of the system and deriving from it the V&V building blocks. 2. Breaking down the system into independent factors and connecting to each factor the relevant part of the physical structures. For each component in the structures it is necessary to identify its functionality and whether if the specification comply with demands. 3. Building computational, analytical and functional models which describe the system, sub-system and its components behavior and sensitivity analysis. 4. Experimental validation for individual sub-systems and components. The purposes are to verify reliability of the models, to validate the margin of safety needed and to find out the failure threshold. 5. Experimental validation at a higher level. The purposes of this stage are to examine the internal and external interfaces, to verify the approach of the separation of parameters and to validate the system functionality. This new approach will be demonstrated on an electromechanical system.

BackgroundComplex systems approaches are increasingly used in health promotion and noncommunicable disease prevention research, policy and practice. Questions emerge as to the best ways to take a complex systems approach, specifically with respect to population physical activity (PA). Using an Attributes Model is one way to understand complex systems. We aimed to examine the types of complex systems methods used in current PA research and identify what methods align with a whole system approach as reflected by an Attributes Model.MethodsA scoping review was conducted and two databases were searched. Twenty-five articles were selected and data analysis was based upon the following: the complex systems research methods used, research aims, if participatory methods were used and evidence of discussion regarding attributes of systems.ResultsThere were three groups of methods used: system mapping, simulation modelling and network analysis. System mapping methods appeared to align best with a whole system approach to PA promotion because they largely aimed to understand complex systems, examined interactions and feedback among variables, and used participatory methods. Most of these articles focused on PA (as opposed to integrated studies). Simulation modelling methods were largely focused on examining complex problems and identifying interventions. These methods did not generally focus on PA or use participatory methods. While network analysis articles focused on examining complex systems and identifying interventions, they did not focus on PA nor use participatory methods. All attributes were discussed in some way in the articles. Attributes were explicitly reported on in terms of findings or were part of discussion and conclusion sections. System mapping methods appear to be well aligned with a whole system approach because these methods addressed all attributes in some way. We did not find this pattern with other methods.ConclusionsFuture research using complex systems methods may benefit from applying the Attributes Model in conjunction with system mapping methods. Simulation modelling and network analysis methods are seen as complementary and could be used when system mapping methods identify priorities for further investigation (e.g. what interventions to implement or how densely connected relationships are in systems).

Information plays a critical role in complex biological systems. Complex systems like immune systems and ant colonies co-ordinate heterogeneous components in a decentralized fashion. How do these distributed decentralized systems function? One key component is how these complex systems efficiently process information. These complex systems have an architecture for integrating and processing information coming in from various sources and points to the value of information in the functioning of different complex biological systems. This article proposes a role for information processing in questions around the origin of life and suggests how computational simulations may yield insights into questions related to the origin of life. Such a computational model of the origin of life would unify thermodynamics with information processing and we would gain an appreciation of why proteins and nucleotides evolved as the substrate of computation and information processing in living systems that we see on Earth. Answers to questions like these may give us insights into non-carbon based forms of life that we could search for outside Earth. We hypothesize that carbon-based life forms are only one amongst a continuum of systems in the universe. Investigations into the role of computational substrates that allow information processing is important and could yield insights into: 1) novel non-carbon based computational substrates that may have life-like properties, and 2) how life may have actually originated from non-life on Earth. Life may exist as a continuum between non-life and life and we may have to revise our notion of life and how common it is in the universe. Looking at life or phenomenon through the lens of information theory may yield a broader view of life.

The purpose of the paper is to show the need for assessing the quality of functioning of telecommunication systems in transport. Show that the problem of the quality of functioning of complex systems, which include telecommunications, is closely related to the reliability of both its individual elements and the system as a whole. From the set of existing parameters for assessing reliability, identify those that have a significant impact on the quality of functioning of transport telecommunication systems. Propose a methodology for assessing such parameters. Parameters for assessing the reliability of complex technical systems in relation to telecommunications were analyzed. The main indicators of the quality of the functioning of telecommunication systems are revealed. It is shown that the functioning of the system can be characterized by an indicator of technical efficiency, reflecting the reliability and recoverability, as well as the correctness of information processing, and quantified by the product of the probabilities of the corresponding private events, which can be considered independent. The calculations of the probability that the execution of the task will not be disrupted due to the unreliability of the system, the probability of the task being completed by the system with the failure-free operation of the equipment for a given time, as well as the probability that the system, being serviceable in the initial state with a given probability, will then work without failure for the specified time and will ensure the correct solution (or execution) of the task are presented. An integral indicator of the quality of functioning for transport telecommunication systems has been developed. The classification of the quality indicators of functioning of telecommunication systems from the position of a set of complex technical systems and man-machine complexes is given.KeywordsTelecommunication systemTransportReliability indicatorQualityFunctioningEfficiencyComplex system

Complex systems involve multiple aspects such as domain knowledge, constraints, human roles and interaction, life cycle and process management, and organizational and social factors. Many complex systems are dynamic and need to cater for online, run time, and ad hoc requests. With the involvement of social intelligence and its complexities, such complex systems need to consider reliability, reputation, risk, privacy, security, trust, and actionability of problem-solving solutions. Research in one area can actually stimulate, complement, and enhance research in another. A typical example is agent mining technology (Cao L, Gorodetsky V, Mitkas P, IEEE Intell Syst, May/June, 2009; Cao L, Gorodetsky V, Mitkas P, Guest editors’ introduction: Agents and data mining, May/June, 2009; Cao L (ed), Data mining and multiagent integration. Springer, 2009; Gorodetsky V, et al (eds), Autonomous intelligent systems: agents and data mining. LNAI, vol 4476. Springer, 2007), which synergizes the ubiquitous intelligence for handling complex intelligent problems and systems through the combined strengths of data mining, machine learning, and multiagent systems. Other typical examples that involve ubiquitous intelligence include open complex intelligent systems (Cao L, Dai R, Open complex intelligent systems. Post & Telecom Press, 2008; Qian X, Yu J, Dai R, Chin J Nat 13(1):3–10, 1990), domain-driven actionable knowledge discovery (Cao L et al, IEEE Intell Syst 22(4):78–89, 2007), combined mining for discovering complex patterns (Cao L, Zhang H, Zhao Y, Zhang C, General frameworks for combined mining: case studies in e-government services. Submitted to ACM TKDD, 2008), and ubiquitous computing (Poslad S, Ubiquitous computing: smart devices, environments and interactions. Wiley, 2009).KeywordsUbiquitous IntelligenceHuman Social IntelligenceActionable Knowledge DiscoveryIntelligence DomainStock Data MiningThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The relationship between form and function can have profound effects on evolutionary dynamics and such effects may differ for simple versus complex systems. In particular, functions produced by multiple structural configurations (many-to-one mapping, MTOM) may dampen constituent trade-offs and promote diversification. Unfortunately, we lack information about the genetic architecture of MTOM functional systems. The skulls of teleost fishes contain both simple (lower jaw levers) as well as more complex (jaws modeled as 4-bar linkages) functional systems within the same craniofacial unit. We examined the mapping of form to function and the genetic basis of these systems by identifying quantitative trait loci (QTL) in hybrids of two Lake Malawi cichlid species. Hybrid individuals exhibited novelty (transgressive segregation) in morphological components and function of the simple and complex jaw systems. Functional novelty was proportional to the prevalence of extreme morphologies in the simple levers; by contrast, recombination of parental morphologies produced transgression in the MTOM 4-bar linkage. We found multiple loci of moderate effect and epistasis controlling jaw phenotypes in both the simple and complex systems, with less phenotypic variance explained by QTL for the 4-bar. Genetic linkage between components of the simple and complex systems partly explains phenotypic correlations and may constrain functional evolution.

The objective of the present study is to design a basic software and hardware complex (SHC) of a seismometric monitoring system for buildings and structures recommended for serial production. To do this, the authors used modern methods and principles of designing software and hardware systems. The system is methodologically based on the engineering-seismometric method. This method uses recording of spatial vibrations of an object as a result of microseisms of natural and anthropogenic origin. Next, dynamic and elastic characteristics of the structure are determined by vibrations that make it possible to evaluate its technical condition. As a result of conducted studies, the authors defined the main technical and functional requirements for the monitoring system for a wide range of applications. A three-level structure of a modular technical condition monitoring system is proposed for discussion. This structure makes it possible to create an extensible open system in which the number of measuring channels can be easily increased by increasing the number of plugin standard modules. In addition, the system can both record seismic events, earthquakes, and perform seismometric monitoring by microseisms. In order to represent the main processes and structure of the proposed basic complex of the system in the case of its full operation, the authors developed a functional model of the system. The model is based on a data flow diagram that describes the processes of collecting, processing, storing, analyzing, and presenting seismometric monitoring data. The functioning of the proposed complex is briefly described. The input data of the complex are the vibrations of the structure recorded at observation points using three-component geophones. Then, the seismic signal recorder collects, amplifies, digitizes, and transmits data to the server. The server either records seismic events and then evaluates their impact on the structure or carries out a planned recording of microseisms in order to monitor the technical condition of the structure and stores vibration data in the corresponding files. If an earthquake is detected, the system notifies the responsible personnel. Records of microseisms are used by the data processing software to compute the statistical parameters of vibrations and complex transfer functions according to the spectra of which the operator manually selects the values of natural frequencies. Seismic monitoring data processing software evaluates elastic characteristics by a number of natural frequencies using a mathematical design model of the structure vibrations. On the basis of the analysis of the change in the obtained dynamic and elastic characteristics and taking into account the effect of external factors, the software generates information for monitoring the technical condition of the structure. These results, as well as data of the evaluation of the event impact on the structure, are the output data of the system.

*Correspondence: cgg@unam.mx 1Universidad Nacional Autonoma de Mexico, A.P. 20-726, 01000 Mexico city, D.F, Mexico Full list of author information is available at the end of the article Complex systems and networks Complex Adaptive Systems (CAS) or complex systems are characterized by the interactions between their numerous elements. The word ‘complex’ comes from the Latin plexus which means entwined. In other words, it is difficult to correlate global properties of complex systems with the properties of the individual constituent components. This is primarily because the interactions between these individual elements partly determine the future states of the system (Gershenson 2013). If these interactions are not included in the developed models, the models would not be an accurate reflection of the modelled phenomenon. While numerous techniques and frameworks for modeling complex systems have previously been devised (Niazi 2011), clearly one of the most explicit and intuitive methodology is the modeling of interactions using networks (Niazi and Hussain 2012). Networks consist of nodes or vertices, which can be used to represent elements, and links or edges, which usually represent interactions or relations between the elements. In this context, networks represent the structure of complex systems; how elements interact. However, networks can also be used to represent the dynamics or function of complex systems, e.g. considering nodes as states and links as transitions. Thus, the same analysis can be applied to the structure and the function of networks. Understanding the relationship between structure and function is one of the major open questions across sciences, which can also be posed using networks: how do changes in the structural network affect the state network? (Boccaletti et al. 2006; Gershenson 2012). For example, what will be the effect of knocking out a gene in the behavior of a cell? Several systems change their structure over time, and their properties can be modelled with temporal networks (Holme and Saramaki 2012). Likewise, there are several instances when the structural changes are triggered by the state of the network, as has been studied in adaptive networks (Gross and Sayama 2009). With not much more than a decade of network research, there are already numerous applications of networks in diverse areas, such as epidemiology (Colizza et al. 2007; Christakis and Fowler 2007; Pastor-Satorras and Vespignani 2001), human mobility (Gonzalez et al. 2008), social networks (Huberman et al. 2009; Niazi and Hussain 2011), artificial life (Gershenson and Prokopenko 2011), life sciences (Bullmore and Sporns 2009; Gershenson 2004; Guimera and Nunes Amaral 2005; Montoya et al. 2006), theory of

The name of this panel session called for a discussion on the progress in human-system interaction. It appears that the progress in that area is invariably associated with intelligent agents, a subject which reoccurred in each panel presentation. An increased power of computing machinery together with an advancement of technology in other areas opened a new frontier for human factors research in complex human machine systems employing high degree of automation as well as humans. Intelligent agents entered various stages of human interaction with complex system, such as the design of automated systems, operation and maintenance of complex systems, and training of humans which interact with complex systems. Agents, by the earliest definition are artifacts that have a very specialized function, usually quite complex. In the human-machine systems intelligent agents often serve as computer interface agents, systems that can serve as go-betweens because they posses some specialized skills. There are agents that serve as assistants to humans interacting and controlling complex systems, and agents that serve as tutors training the humans to operate systems. The set of tasks and applications where intelligent agents could be employed is virtually unlimited.

Synthetic biology is an emerging field blending approaches and concepts derived from classic engineering disciplines with modern biological approaches. Concepts of modularity and orthogonality, i.e. the transfer of simple building blocks between unrelated chassis (host organisms), are guiding principles for the design and construction of artificial biological systems, which in their ultimate implementation can be artificial organisms. Synthetic biology is not only leading the way towards the engineering of useful organisms that serve human purposes, it is also a new way of approaching basic scientific questions to understand complex biological systems. The classic reductionist methodology by which scientists have dissected complex systems to understand their properties through understanding the functionality of isolated components, finds its counterpart in synthetic biology. If we can build complex biological processes, systems, and ultimately organisms from simple, fully understood functional modules using a set of defined rules, we must fully understand the system. At first this approach may sound almost naïve as with near certainty scientists will encounter spectacular 'failures' on the way to building complex biological systems. Undoubtedly, the result of synthetic biology efforts will be more than the sum of the individual components giving rise to complex systems with novel emergent properties, many of which are unexpected or even undesired. However, the process of learning from those 'failures' often through predictive modeling and simulation studies in parallel to the actual assembly and testing of artificial biological systems, will lead to novel insights into the function of complex biological systems in general. Plant and algal cells are complex with their extra organelle, the plastid, and are highly sophisticated in their metabolism enabling them to convert light, CO2 and minerals into the building blocks of cells, produce all oxygen in the atmosphere, thousands of specialized chemicals including drugs, and energy-rich compounds that fuel life on earth. While engineers have been dabbling for many years in the redesign of bacterial and yeast chassis with novel properties, the application of synthetic biology to photosynthetic organisms is just beginning. Therefore, it seems timely to provide an overview of the state of the art of 'Synthetic Biology for Basic and Applied Plant Research' in this special issue of The Plant Journal. Next Generation Sequencing has given us a nearly unlimited number of genomic blueprints for photosynthetic bacteria, algae and plants and this provides the raw material for synthetic biology. Tools for recombining of genes and introducing them into an increasing number of photosynthetic chassis including organelles such as chloroplasts, are available and no longer an impediment to the application of synthetic biology to plants. One revolutionary technique, the introduction of the CRISPR/CAS system for genome editing is now being applied to edit not only the plant genome, but also the transcriptome and epigenome as discussed by Puchta (2016). Bacterial microcompartments, first discovered as carboxysomes in cyanobacteria, provide an important platform for the engineering of synthetic modules. They can encapsulate enzymes, concentrate substrates, and help in the avoidance of toxic products as Gonzalez-Esquer et al. (2016) describe. Cyanobacteria address one key problem that all photosynthetic organisms encounter, the natural inefficiency of the carbon-fixing enzyme RubBisCO, by encapsulating this enzyme in carboxysomes, which increases the local concentration of CO2 around the enzyme. Plants do not have a carboxysome-based carbon concentration mechanism to overcome the limitation of photosynthesis through RubBisCO's inefficiency. The solution could be to introduce this bacterial microcompartment into chloroplasts of crop plants and synthetic biology efforts towards this aim are well under way as described by Hanson et al. (2016). A subset of plants has evolved their own way of overcoming this problem by prefixing carbon using a more efficient enzyme than RubBisCO. This carbon concentration mechanism requires the compartmentalization of different sets of enzymes in different cells of the leaf, and this overall approach is referred to as C4-syndrome of C4 plants, because the CO2 is first fixed into a four-carbon compound rather than the three-carbon compound produced first by RuBisCO in C3 plants. Some of the important crop plants that feed the world are C4 plants, such as maize, but many are not, including wheat and rice. The solution is to engineer C4 photosynthesis in a C3 chassis and as Schuler et al. (2016) describe, efforts are well underway by applying synthetic biology. Introduction of orthogonal biosynthetic pathways into photosynthetic organelles and bacteria to enhance their synthetic repertoires requires a deep knowledge of the regulation of photosynthesis, as the balance of ATP/and NADPH and the nature of the carbon sink are critical for the efficiency of photosynthesis. Nielson and coworkers describe how optimization of carbon flux and reductant are critical elements in engineering cyanobacteria and chloroplasts to sustainably produce novel chemicals (Nielsen et al., 2015). Plants are capable of making a seemingly unlimited number of specialized compounds to defend themselves against pathogens or herbivores and many of these compounds have been used by humans for thousands of years, e.g. as drugs. One particular compound class, the terpenoids, provides an example of the amazing natural combinatorial chemistry that plants are capable of. Applying synthetic biology principles of modularity and orthogonality, plant engineers are now capable of recombining different modules of terpenoid biosynthesis from different sources into new chassis to engineer plants that produce new-to-nature compounds as Arendt et al. (2016) describe. Another spectacular success in recombining modules of genes derived from different plants, algae, and fungi into a new chassis, the industrial crop Camelina, is the production of oils with a near natural composition of healthy oils found in fish as summarized by Haslam et al. (2016). With this accomplishment, important sustainability and human health questions can be addressed. These include improving the sustainability of the aquaculture industry for the production of fish rich in omega-3 oils with well-known health benefits when part of the human diet. Another example of addressing pressing problems for humankind is the generation of sustainable feed-stocks for energy production, independent of fossil fuels. For this reason, many scientists are currently pursuing the engineering of dedicated biofuel crops through the application of synthetic biology principles as summarized by Shih et al. (2016). Plant signaling pathways are highly interconnected and redundant, and hence often hard to dissect using the classical reductionistic approaches. Synthetic Biology offers a new way to explore individual signaling pathways by reassembling them bottom up from modules in non-interfering backgrounds of new chassis. Braguy and Zurbriggen (2016) describe this approach in detail. Ultimately, understanding how signaling pathways feed into programmable plant genetic circuits will be essential for the engineering of plants to be more efficient or to produce novel compounds. Medford and Prasad (2016) explain how genetic parts such as promoters and other regulatory elements can be tested and their assembly into genetic circuits simulated. The list of examples and approaches described in this special issue of The Plant Journal is comprehensive. Our intention is that this special issue will explain key principles and areas of plant synthetic biology to guide the reader and future contributors of The Plant Journal in embracing these approaches for both fundamental and applied plant science. Other areas of interest not covered here include synthetic consortia, the synthetic interaction of photosynthetic and heterotrophic organisms beyond naturally occurring symbioses. As we learn to understand how the microbiome affects plant growth, synthetic biology approaches may be key in learning more about these complex interactions, a topic that certainly falls with in the scope of The Plant Journal. With the expansion of the current field of plant synthetic biology, The Plant Journal welcomes the submission of basic research papers applying synthetic biology to further our understanding of the full biological complexity of photosynthetic organisms and their complex biotic and abiotic interaction with the environment.

Chapter 9 - Computational fractional-order calculus and classical calculus AI for comparative differentiability prediction analyses of complex-systems-grounded paradigm

As most of practical systems have high complexity, complex systems have become a rapidly growing area of mathematics and attracted many researchers. The study of complex systems not only has an important theoretical interest but also is motivated by problems from applied mathematics including physics, chemistry, astronomy, technology, and natural and social sciences. It should be noted that some major problems have not been fully investigated, such as the behavior of stability, synchronization, bifurcation, and chaos control for complex systems, as well as their applications in, for example, communication and bioinformatics. The special issue contains seven papers; of these, three of the papers are related to application analysis of complex systems to the real world problems. One paper studies the synchronization of chaotic complex systems with fractional-order. One paper investigates the consensus problem for nonlinear complex systems. Another paper provides an approach to determine the unique 3-uniform linear hypertree with the maximum Estrada index. Finally, a paper provides interior principles to calculate the leading elements of the aliased effect-number pattern. In the paper “Results for Two-Level Designs with General Minimum Lower-Order Confounding,” the authors study the interior principles of calculating the leading elements in 1#C1 and 2#C2 aliased effect-number pattern. Also, their mathematical formulations are obtained for every lower-order confounding 2n−m design according to the two different cases. In the paper “On the Maximum Estrada Index of 3-Uniform Linear Hypertrees,” authors give some basic definitions on the Estrada index of hypergraph and then formulate an algorism for determining the unique 3-uniform linear hypertree with the maximum Estrada index. In the paper “Consensus of Nonlinear Complex Systems with Edge Betweenness Centrality Measure under Time-Varying Sampled-Data Protocol,” by constructing a suitable Lyapunov-Krasovskii functional and using linear matrix inequality technique, the authors propose a new consensus criterion for nonlinear complex systems with edge betweenness centrality measure. Finally, a numerical example is provided to illustrate the effectiveness of the proposed consensus schemes. In the paper “One Adaptive Synchronization Approach for Fractional-Order Chaotic System with Fractional-Order 1 < q < 2,” based on a new stability result of equilibrium point in nonlinear fractional-order systems, the authors investigate the adaptive synchronization for the fractional-order Lorenz chaotic system with fractional-order 1 < q < 2. Numerical simulations show the feasibility of the proposed adaptive synchronization scheme. In the paper “A New Chaotic Map and Its Application on Image Encryption,” the authors present a novel approach to create the new chaotic map and then applied it to image encryption. Compared with traditional classic one-dimensional chaotic map like Logistic Map and Tent Map, this newly created chaotic map demonstrates many better chaotic properties for encryption. The simulation results and security analysis show that such method not only meets the requirement of imagine encryption but also has better security, which is very useful for general applications. In the paper “Description and Application of a Mathematical Method for the Analysis of Harmony,” after briefly introducing the basic concepts of harmony theory, the authors expound the five essential elements for the quantitative description of harmony issues in water resources management: harmony participant, harmony objective, harmony regulation, harmony factor, and harmony action. Furthermore, a basic mathematical equation for the harmony degree is introduced. In the paper “A Learning Framework of Nonparallel Hyperplanes Classifier,” the authors concerned the learning framework of nonparallel hyperplanes support vector machines (SVM) for binary classification and multiclass classification. The given framework not only includes twin SVM and its many deformation versions but also extends them into multiclass classification problem with loss functions or different parameters. The numerical experiments on several artificial and benchmark datasets indicate that the introduced frameworks not only are fast but also have good generalization.

Aim. The article deals with the problematic aspects of planning the management of the functioning and modernization of existing inherited and created complex technical systems in a rapidly changing environment of modern science-intensive industries and limitations of time, material and information resources. Materials and methods. The article proposes the results of system analysis and the selected original meaningful interpretation of the formalization and design (engineering) scenarios for planning the operation and modernization management based on the theory of monitoring and managing the structural dynamics of complex technical systems. Results. A systematic analysis of the current state of affairs in the use and modernization of complex technical systems has been carried out. The necessity of automating the processes of planning their management and modernization, as well as the conceptualization of plans using the fundamental principles of the theory of artificial intelligence, is determined. The requirements and starting points for formalizing and designing scenarios for planning proactive management based on the key principles of business process models are formed. Conclusions. The proposed meaningful interpretation of the engineering of conceptualization of situational planning for proactive control of the functioning and modernization of complex technical systems has great prospects in the further stages of building an interdisciplinary theory of integrated monitoring and control of the structural dynamics of complex technical systems, as well as in the applied results of automating the planning task.

&lt;p&gt;This thesis encompasses research on Artificial Intelligence in support of automating scientific discovery in the fields of biology and medicine. At the core of this research is the ongoing development of a general-purpose artificial intelligence framework emulating various facets of human-level intelligence necessary for building cross-domain knowledge that may lead to new insights and discoveries. To learn and build models in a data-driven manner, we develop a general-purpose learning framework called Syntactic Nonparametric Analysis of Complex Systems (SYNACX), which uses tools from Bayesian nonparametric inference to learn the statistical and syntactic properties of biological phenomena from sequence data. We show that the models learned by SYNACX offer performance comparable to that of standard neural network architectures. For complex biological systems or processes consisting of several heterogeneous components with spatio-temporal interdependencies across multiple scales, learning frameworks like SYNACX can become unwieldy due to the the resultant combinatorial complexity. Thus we also investigate ways to robustly reduce data dimensionality by introducing a new data abstraction. In particular, we extend traditional string and graph grammars in a new modeling formalism which we call Simplicial Grammar. This formalism integrates the topological properties of the simplicial complex with the expressive power of stochastic grammars in a computation abstraction with which we can decompose complex system behavior, into a finite set of modular grammar rules which parsimoniously describe the spatial/temporal structure and dynamics of patterns inferred from sequence data.&lt;/p&gt;

The Law of Requisite Variety is a mathematical theorem relating the number of control states of a system to the number of variations in control that is necessary for effective response. The Law of Requisite Variety does not consider the components of a system and how they must act together to respond effectively. Here we consider the additional requirement of scale of response and the effect of coordinated versus uncoordinated response as a key attribute of complex systems. The components of a system perform a task, with a number of such components needed to act in concert to perform subtasks. We apply the resulting generalization—a Multiscale Law of Requisite Variety—to understanding effective function of complex biological and social systems. This allows us to formalize an understanding of the limitations of hierarchical control structures and the inadequacy of central control and planning in the solution of many complex social problems and the functioning of complex social organizations, e.g., the military, healthcare, and education systems. © 2004 Wiley Periodicals, Inc. Complexity 9: 37–45, 2004