Cooperative Approach for Composite Ontology Mapping

This paper proposes a cooperative approach for composite ontology mapping. We first present an extended classification of automated ontology matching and propose an automatic composite solution for the matching problem based on cooperation. In our proposal, agents apply individual mapping algorithms and cooperate in order to change their individual results. We assume that the approaches are complementary to each other and their combination produces better results than the individual ones. Next, we compare our model with three state of the art matching systems. The results are promising specially for what concerns precision and recall. Finally, we propose an argumentation formalism as an extension of our initial model. We compare our argumentation model with the matching systems, showing improvements on the results

A Demand-Supply Cooperative Responding Strategy in Power System with High Renewable Energy Penetration

Industrial demand response (IDR) plays an important role in promoting the utilization of renewable energy (RE) in power systems. However, it will lead to power adjustments on the supply side, which is also a non-negligible factor in affecting RE utilization. To comprehensively analyze this impact while enhancing RE utilization, this paper proposes a power demand-supply cooperative response (PDSCR) strategy based on both day-ahead and intraday time scales. The day-ahead PDSCR determines a long-term scheme for responding to the predictable trends in RE supply. However, this long-term scheme may not be suitable when uncertain RE fluctuations occur on an intraday basis. Regarding intraday PDSCR, we formulate a profit-driven cooperation approach to address the issue of RE fluctuations. In this context, unreasonable profit distributions on the demand-supply side would lead to the conflict of interests and diminish the effectiveness of cooperative responses. To mitigate this issue, we derive multi-individual profit distribution marginal solutions (MIPDMSs) based on satisfactory profit distributions, which can also maximize cooperative profits. Case studies are conducted on an modified IEEE 24-bus system and an actual power system in China. The results verify the effectiveness of the proposed strategy for enhancing RE utilization, via optimizing the coordination of IDR flexibility with generation resources.Comment: Accepted by IEEE Transactions on Control Systems Technolog

Control in distribution networks with demand side management

The way in which electricity networks operate is going through a period of significant change. Renewable generation technologies are having a growing presence and increasing penetrations of generation that are being connected at distribution level. Unfortunately, a renewable energy source is most of the time intermittent and needs to be forecasted. Current trends in Smart grids foresee the accommodation of a variety of distributed generation sources including intermittent renewable sources. It is also expected that smart grids will include demand management resources, widespread communications and control technologies required to use demand response are needed to help the maintenance in supply-demand balance in electricity systems. Consequently, smart household appliances with controllable loads will be likely a common presence in our homes. Thus, new control techniques are requested to manage the loads and achieve all the potential energy present in intermittent energy sources. This thesis is focused on the development of a demand side management control method in a distributed network, aiming the creation of greater flexibility in demand and better ease the integration of renewable technologies. In particular, this work presents a novel multi-agent model-based predictive control method to manage distributed energy systems from the demand side, in presence of limited energy sources with fluctuating output and with energy storage in house-hold or car batteries. Specifically, here is presented a solution for thermal comfort which manages a limited shared energy resource via a demand side management perspective, using an integrated approach which also involves a power price auction and an appliance loads allocation scheme. The control is applied individually to a set of Thermal Control Areas, demand units, where the objective is to minimize the energy usage and not exceed the limited and shared energy resource, while simultaneously indoor temperatures are maintained within a comfort frame. Thermal Control Areas are overall thermodynamically connected in the distributed environment and also coupled by energy related constraints. The energy split is performed based on a fixed sequential order established from a previous completed auction wherein the bids are made by each Thermal Control Area, acting as demand side management agents, based on the daily energy price. The developed solutions are explained with algorithms and are applied to different scenarios, being the results explanatory of the benefits of the proposed approaches

Methodology and Software for Interactive Decision Support

These Proceedings report the scientific results of an International Workshop on "Methodology and Software for Interactive Decision Support" organized jointly by the System and Decision Sciences Program of IIASA and The National Committee for Applied Systems Analysis and Management in Bulgaria. Several other Bulgarian institutions sponsored the workshop -- The Committee for Science to the Council of Ministers, The State Committee for Research and Technology and The Bulgarian Industrial Association. The workshop was held in Albena, on the Black Sea Coast. In the first section, "Theory and Algorithms for Multiple Criteria Optimization," new theoretical developments in multiple criteria optimization are presented. In the second section, "Theory, Methodology and Software for Decision Support Systems," the principles of building decision support systems are presented as well as software tools constituting the building components of such systems. Moreover, several papers are devoted to the general methodology of building such systems or present experimental design of systems supporting certain class of decision problems. The third section addresses issues of "Applications of Decision Support Systems and Computer Implementations of Decision Support Systems." Another part of this section has a special character. Beside theoretical and methodological papers, several practical implementations of software for decision support have been presented during the workshop. These software packages varied from very experimental and illustrative implementations of some theoretical concept to well developed and documented systems being currently commercially distributed and used for solving practical problems

Reinforcement learning for trading dialogue agents in non-cooperative negotiations

Recent advances in automating Dialogue Management have been mainly made in cooperative environments -where the dialogue system tries to help a human to meet their goals. In non-cooperative environments though, such as competitive trading, there is still much work to be done. The complexity of such an environment rises as there is usually imperfect information about the interlocutors’ goals and states. The thesis shows that non-cooperative dialogue agents are capable of learning how to successfully negotiate in a variety of trading-game settings, using Reinforcement Learning, and results are presented from testing the trained dialogue policies with humans. The agents learned when and how to manipulate using dialogue, how to judge the decisions of their rivals, how much information they should expose, as well as how to effectively map the adversarial needs in order to predict and exploit their actions. Initially the environment was a two-player trading game (“Taikun”). The agent learned how to use explicit linguistic manipulation, even with risks of exposure (detection) where severe penalties apply. A more complex opponent model for adversaries was also implemented, where we modelled all trading dialogue moves as implicitly manipulating the adversary’s opponent model, and we worked in a more complex game (“Catan”). In that multi-agent environment we show that agents can learn to be legitimately persuasive or deceitful. Agents which learned how to manipulate opponents using dialogue are more successful than ones which do not manipulate. We also demonstrate that trading dialogues are more successful when the learning agent builds an estimate of the adversarial hidden goals and preferences. Furthermore the thesis shows that policies trained in bilateral negotiations can be very effective in multilateral ones (i.e. the 4-player version of Catan). The findings suggest that it is possible to train non-cooperative dialogue agents which successfully trade using linguistic manipulation. Such non-cooperative agents may have important future applications, such as on automated debating, police investigation, games, and education

On-Farm Water Management Game With Heuristic Capabilities

A modern computer-based simulation tool (WaterMan) in the form of a game for on-farm water management was developed for application in training events for farmers, students, and irrigators. The WaterMan game utilizes an interactive framework, thereby allowing the user to develop scenarios and test alternatives in a convenient, risk-free environment. It includes a comprehensive soil water and salt balance calculation algorithm. It also employs heuristic capabilities for modeling all of the important aspects of on-farm water management, and to provide reasonable scores and advice to the trainees. Random events (both favorable and unfavorable) and different strategic decisions are included in the game for more realism and to provide an appropriate level of challenge according to player performance. Thus, the ability to anticipate the player skill level, and to reply with random events appropriate to the anticipated level, is provided by the heuristic capabilities used in the software. These heuristic features were developed based on a combination of two artificial intelligence approaches: (1) a pattern recognition approach; and (2) reinforcement learning based on a Markov Decision Processes approach, specifically, the Q-learning method. These two approaches were combined in a new way to account for the difference in the effect of actions taken by the player and action taken by the system on the game world. The reward function for the Q-learning method was modified to reflect the anticipated type of the WaterMan game as what is referred to as a partially competitive and partially cooperative game. Twenty-two different persons classified under three major categories (1) practicing farmers; (2) persons without an irrigation background; and (3) persons with an irrigation background, were observed while playing the game, and each of them filled out a questionnaire about the game. The technical module of the game was validated in two ways: through conducting mass balance calculations for soil water content and salt content over a period of simulation time, and through comparing the WaterMan technical module output data in calculating the irrigation requirements and the use of irrigation scheduling recommendations with those obtained from the same set of input data to the FAO CropWat 8 software. The testing results and the technical validation outcomes demonstrate the high performance of the WaterMan game as a heuristic training tool for on-farm water management

Competition vs. collaboration in the generation and adoption of a sequence of new technology

Although there is quite a rich literature relating to competitive innovation there is relatively little relating to technological collaboration. However, ignoring collaborative possibilities may result in overestimation of the importance of selfinnovation. This thesis is therefore mainly concerned with the determinants of collaboration in innovation, taking both a theoretical and an empirical approach. The empirics relate to the manufacturing industry in a Chinese region. The thesis is particularly innovative in emphasising how collaboration costs will be shared when collaboration occurs. We provide a game theoretic exploration of the decisions of firms on whether to compete or collaborate in the generation and adoption of a sequence of new technologies. Different from the models proposed by Vickers, who concentrates upon process innovation and a two-strategy (innovation or do nothing) set, our game theory model emphasises product innovation and either a three-strategy set (innovation, collaboration, and do nothing), or a fourstrategy set (innovation, collaboration, imitation and do nothing). In particular, MATLAB programming is employed for generating the equilibrium solution for each strategy set. We found that the relationship between imitation and collaboration and collaboration cost is not univariate. It depends upon the market type and various market characteristics, such as technology gap, technology level, the product substitution index, transaction costs and the discount rate of price sensitiveness. The results also show that the elasticity of collaboration opportunity with respect to transaction costs in a persistent dominance market is much greater than in an action reaction market. By using data on manufacturing in a Chinese region from 2005 to 2007, derived from the China Innovation Survey and the Annual Corporate Financial Survey, we empirically explored innovation and collaboration patterns. Three factors, innovative ability, absorptive capacity, and catching up capacity were proposed to positively affect both innovation and collaboration. This led to six hypotheses, which were tested using a number of econometric models encompassing selection bias, timing, and dynamics issues. The major finding from the empirical models suggests that innovative ability, absorptive capacity and catching up capacity all impact significantly and positively on collaboration, whilst innovation is positively related only to absorptive capacity. Also, we found that collaboration cost may increase with R&D, employees‘ education, the technology gap and collaboration cost in previous periods, but decrease with transaction cost, patents held, the technology level and perceived price. The thesis makes three contributions. Theoretically, our game theory model not only extends the understanding of the impacts of collaboration possibilities and collaboration cost in dynamic game theory, but also clarifies the impacts of transaction costs and imitation (and thus intellectual property rights (IPR)) on the outcome. Empirically, by introducing new data our work is the first to investigate collaboration patterns and collaboration cost sharing strategies in a mid-income level developing country. Last but not least, using MATLAB animation programming to simplify the calculation process of the game theory equilibrium may be considered as a methodological contribution