Competitive multi-agent search

textWhile evolutionary computation is well suited for automatic discovery in engineering, it can also be used to gain insight into how humans and organizations could perform more effectively. Using a real-world problem of innovation search in organizations as the motivating example, this dissertation formalizes human creative problem solving as competitive multi-agent search. It differs from existing single-agent and team-search problems in that the agents interact through knowledge of other agents' searches and through the dynamic changes in the search landscape caused by these searches. The main hypothesis is that evolutionary computation can be used to discover effective strategies for competitive multi-agent search. This hypothesis is verified in experiments using an abstract domain based on the NK model, i.e. partially correlated and tunably rugged fitness landscapes, and a concrete domain in the form of a social innovation game. In both domains, different specialized strategies are evolved for each different competitive environment, and also strategies that generalize across environments. Strategies evolved in the abstract domain are more effective and more complex than hand-designed strategies and one based on traditional tree search. Using a novel spherical visualization of the fitness landscapes of the abstract domain, insight is gained about how successful strategies work, e.g. by tracking positive changes in the landscape. In the concrete game domain, human players were modeled using backpropagation, and used as opponents to create environments for evolution. Evolved strategies scored significantly higher than the human models by using a different proportion of actions, providing insights into how performance could be improved in social innovation domains. The work thus provides a possible framework for studying various human creative activities as competitive multi-agent search in the future.Computer Science

Hybridizations within a graph based hyper-heuristic framework for university timetabling problems

A significant body of recent literature has explored various research directions in hyper-heuristics (which can be thought as heuristics to choose heuristics). In this paper, we extend our previous work to construct a unified graph-based hyper-heuristic (GHH) framework, under which a number of local search-based algorithms (as the high level heuristics) are studied to search upon sequences of low-level graph colouring heuristics. To gain an in-depth understanding on this new framework, we address some fundamental issues concerning neighbourhood structures and characteristics of the two search spaces (namely, the search spaces of the heuristics and the actual solutions). Furthermore, we investigate efficient hybridizations in GHH with local search methods and address issues concerning the exploration of the high-level search and the exploitation ability of the local search. These, to our knowledge, represent entirely novel directions in hyper-heuristics. The efficient hybrid GHH obtained competitive results compared with the best published results for both benchmark course and exam timetabling problems, demonstrating its efficiency and generality across different problem domains. Possible extensions upon this simple, yet general, GHH framework are also discussed

Web competitive intelligence methodology

Master’s Degree DissertationThe present dissertation covers academic concerns in disruptive change that causes value displacements in today’s competitive economic environment. To enhance survival capabilities organizations are increasing efforts in more untraditional business value assets such intellectual capital and competitive intelligence. Dynamic capabilities, a recent strategy theory states that companies have to develop adaptive capabilities to survive disruptive change and increase competitive advantage in incremental change phases. Taking advantage of the large amount of information in the World Wide Web it is propose a methodology to develop applications to gather, filter and analyze web data and turn it into usable intelligence (WeCIM). In order to enhance information search and management quality it is proposed the use of ontologies that allow computers to “understand” particular knowledge domains. Two case studies were conducted with satisfactory results. Two software prototypes were developed according to the proposed methodology. It is suggested that even a bigger step can be made. Not only the success of the methodology was proved but also common software architecture elements are present which suggests that a solid base can be design for different field applications based on web competitive intelligence tools

Memory Bounded Open-Loop Planning in Large POMDPs using Thompson Sampling

State-of-the-art approaches to partially observable planning like POMCP are based on stochastic tree search. While these approaches are computationally efficient, they may still construct search trees of considerable size, which could limit the performance due to restricted memory resources. In this paper, we propose Partially Observable Stacked Thompson Sampling (POSTS), a memory bounded approach to open-loop planning in large POMDPs, which optimizes a fixed size stack of Thompson Sampling bandits. We empirically evaluate POSTS in four large benchmark problems and compare its performance with different tree-based approaches. We show that POSTS achieves competitive performance compared to tree-based open-loop planning and offers a performance-memory tradeoff, making it suitable for partially observable planning with highly restricted computational and memory resources.Comment: Presented at AAAI 201

Determinants of patent citations in biotechnology: An analysis of patent influence across the industrial and organizational boundaries

The present paper extends the literature investigating key drivers leading certain patents to exert a stronger influence on the subsequent technological developments (inventions) than other ones. We investigated six key determinants, as (i) the use of scientific knowledge, (ii) the breadth of the technological base, (iii) the existence of collaboration in patent development, (iv) the number of claims, (v) the scope, and (vi) the novelty, and how the effect of these determinants varies when patent influence—as measured by the number of forward citations the patent received—is distinguished as within and across the industrial and organizational boundaries. We conducted an empirical analysis on a sample of 5671 patents granted to 293 US biotechnology firms from 1976 to 2003. Results reveal that the contribution of the determinants to patent influence differs across the domains that are identified by the industrial and organizational boundaries. Findings, for example, show that the use of scientific knowledge negatively affects patent influence outside the biotechnology industry, while it positively contributes to make a patent more relevant for the assignee's subsequent technological developments. In addition, the broader the scope of a patent the higher the number of citations the patent receives from subsequent non-biotechnology patents. This relationship is inverted U-shaped when considering the influence of a patent on inventions granted to other organizations than the patent's assignee. Finally, the novelty of a patent is inverted-U related with the influence the patent exerts on the subsequent inventions granted across the industrial and organizational boundaries

The role of Intangible Assets in the Relationship between HRM and Innovation: A Theoretical and Empirical Exploration

This paper, as far as known, provides a first attempt to explore the role of intellectual capital (IC) and knowledge management (KM) in an integrative way between the relationship of human resource (HR) practices and two types of innovation (radical and incremental). More specifically, the study investigates two sub-components of IC – human capital and organizational social capital. At the same time, four KM channels are discussed, such as knowledge creation, acquisition, transfer and responsiveness.\ud The research is a part of a bigger project financed by the Ministry of Economic Affairs and the province of Overijssel in the Netherlands. The project studies the ‘competencies for innovation’ and is conducted in collaboration with innovative companies in the Eastern part of the Netherlands. \ud An exploratory survey design with qualitative and quantitative data is used for\ud investigating the topic in six companies from industrial and service sector in the region of Twente, the Netherlands. Mostly, the respondents were HR directors. The findings showed that some parts of IC and KM configurations were related to different types of innovation. To make the picture even more complicated, HR practices were sometimes perceived interchangeably with IC and KM by HR directors. Overall, the whole picture about the relationships stays unclear and opens a floor for further research

Intellectual Capital Architectures and Bilateral Learning: A Framework For Human Resource Management

Both researchers and managers are increasingly interested in how firms can pursue bilateral learning; that is, simultaneously exploring new knowledge domains while exploiting current ones (cf., March, 1991). To address this issue, this paper introduces a framework of intellectual capital architectures that combine unique configurations of human, social, and organizational capital. These architectures support bilateral learning by helping to create supplementary alignment between human and social capital as well as complementary alignment between people-embodied knowledge (human and social capital) and organization-embodied knowledge (organizational capital). In order to establish the context for bilateral learning, the framework also identifies unique sets of HR practices that may influence the combinations of human, social, and organizational capital

TEXT MINING AND TEMPORAL TREND DETECTION ON THE INTERNET FOR TECHNOLOGY ASSESSMENT: MODEL AND TOOL

In today´s world, organizations conduct technology assessment (TAS) prior to decision making about investments in existing, emerging, and hot technologies to avoid costly mistakes and survive in the hyper-competitive business environment. Relying on web search engines in looking for relevant information for TAS processes, decision makers face abundant unstructured information that limit their ability to assess technologies within a reasonable time frame. Thus the following qustion arises: how to extract valuable TAS knowledge from a diverse corpus of textual data on the web? To cope with this qustion, this paper presents a web-based model and tool for knowledge mapping. The proposed knowledge maps are constructed on the basis of a novel method of co-word analysis, based on webometric web counts and a temporal trend detection algorithm which employs the vector space model (VSM). The approach is demonstrated and validated for a spectrum of information technologies. Results show that the research model assessments are highly correlated with subjective expert (n=136) assessment (r \u3e 0.91), and with predictive validity valu above 85%. Thus, it seems safe to assume that this work can probably be generalized to other domains. The model contribution is emphasized by the current growing attention to the big-data phenomenon