Chemical programming to eploit chemical Reaction systems for computation

This thesis is on programming approaches to exploit the computational capabilities of chemical systems, consisting of two parts. In the first part, constructive design, research activities on theoretical development of chemical programming are reported. As results of the investigations, general programming principles, named organization-oriented programming, are derived. The idea is to design reaction networks such that the desired computational outputs correspond to the organizational structures within the networks. The second part, autonomous design, discusses on programming strategies without human interactions, namely evolution and exploration. Motivations for this programming approach include possibilities to discover novelty without rationalization. Regarding first the evolutionary strategies, we rather focused on how to track the evolutionary processes. Our approach is to analyze these dynamical processes on a higher level of abstraction, and usefulness of distinguishing organizational evolution in space of organizations from actual evolution in state space is emphasized. As second strategy of autonomous chemical programming, we suggest an explorative approach, in which an automated system is utilized to explore the behavior of the chemical reaction system as a preliminary step. A specific aspect of the system's behavior becomes ready for a programmer to be chosen for a particular computational purpose. In this thesis, developments of autonomous exploration techniques are reported. Finally, we discuss combining those two approaches, constructive design and autonomous design, titled as a hybrid approach. From our perspective, hybrid approaches are ideal, and cooperation of constructive design and autonomous design is fruitful

On the use of biased-randomized algorithms for solving non-smooth optimization problems

Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory management, it is possible to consider stock-outs generated by unexpected demands; and in manufacturing processes and project management, it is frequent that some deadlines cannot be met due to delays in critical steps of the supply chain. However, capacity-, size-, and time-related limitations are included in many optimization problems as hard constraints, while it would be usually more realistic to consider them as soft ones, i.e., they can be violated to some extent by incurring a penalty cost. Most of the times, this penalty cost will be nonlinear and even noncontinuous, which might transform the objective function into a non-smooth one. Despite its many practical applications, non-smooth optimization problems are quite challenging, especially when the underlying optimization problem is NP-hard in nature. In this paper, we propose the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and non-smooth optimization problems in many practical applications. Biased-randomized algorithms extend constructive heuristics by introducing a nonuniform randomization pattern into them. Hence, they can be used to explore promising areas of the solution space without the limitations of gradient-based approaches, which assume the existence of smooth objective functions. Moreover, biased-randomized algorithms can be easily parallelized, thus employing short computing times while exploring a large number of promising regions. This paper discusses these concepts in detail, reviews existing work in different application areas, and highlights current trends and open research lines

Time, space and constructive capabilities. Translating paradoxical innovation requirements into comprehensive organizational arrangements: a socio-cognitive perspective.

When innovating, interaction is beneficial for (1) exploring problem definition spaces and (2) exploiting them. The social processes in which both activities unfold, display paradoxical characteristics which can be addressed by introducing space and time as (organizational) design variables. Complementary arrangements that connect space and time are needed in order for such organizational forms to be sustainable. Propositions in this respect, which build directly on the specific nature of knowledge creation processes, are elaborated.Characteristics; Design; Innovation; Knowledge; Processes; Requirements; Space; Time; Variables;

Placemaking for Cities : Pilot project on the transfer of good practice in community-led placemaking

This report provides an account of the main outcomes of Placemaking 4 Cities (P4C) project and offers critical evaluation of process as well as content of this pilot project in good practice transfer (GPT). In doing so this report draws together detailed descriptions and assessments of the transfer process from learning logs, the mid-term review and exit interviews with P4C participants. The learning logs and the mid-term review are attached in a separate appendix entitled ‘Supporting Documents’. The first part of the report is concerned with an analysis of the results that were achieved. It begins with a presentation of the good practices and anticipated outcomes defined in the baseline study and compares these to the actual results and outputs achieved. The good practices that were adopted and adapted through the transfer are presented at the end of this section. We then review the methodological approach that was adopted to facilitate the GPT, focusing in particular the preparation and executing of the peer review which was central to the transfer process. This is followed by an attempt to assess the impact of the P4C pilot for participating cities of the medium and longer term and the report concludes with a number of recommendations about the design and delivery of future GPT networks.Final Published versio

Exploration through drawings in the conceptual stages of product design

This paper argues that sequences of exploratory drawings - constructed by designer's movements and decisions - trace systematic and logical paths from ideas to designs. This argument has three parts. First, sequences of exploratory sketches produced by product designers, against the same task specification, are analyzed in terms of the cognitive categories of reinterpretation, emergence and abstraction. Second, a computational model is outlined for the process of exploration through drawing and third the model is applied to elucidate the logic in the sequences of exploratory sketches examined earlier

Combinatorial optimization and metaheuristics

Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods

Inspirational Bits - Towards a Shared Understanding of the Digital Material

In any design process, a medium’s properties need to be considered. This is nothing new in design. Still we find that in HCI and interactive systems design the properties of a technology are often glossed over. That is, technologies are black-boxed without much thought given to how their distinctive properties open up design possibilities. In this paper we describe what we call inspirational bits as a way to become more familiar with the design material in HCI, the digital material. We describe inspirational bits as quick and dirty but fully working systems in both hardware and software built with the aim of exposing one or several of the dynamic properties of a digital material. We also show how they provide a means of sharing design knowledge across the members of a multi-disciplined design team