The Cyber-Physical Integrated Society (CPIS) is being formed with the fusion of cyber-space and the real-world. In this paper, we will discuss Data-Driven Decision-Making (DDDM) support systems to solve social problems in the CPIS. First, we introduce a Web of Resources (WoR) that uses Web booking log data for destination data management. Next, we introduce an Internet of Persons (IoP) system to visualize individual and group flows of people by analyzing collected Wi-Fi usage log data. Specifically, we present examples of how WoR and IoP visualize flows of groups of people that can be shared across different industries, including telecommunications carriers and railway operators, and policy decision support for local, short-term events. Finally, the importance of data-driven training of human resources to support DDDM in the future CPIS is discussed.

Considering that different people are different in their linguistic preference and in order to determine the consensus state when using Computing with Words (CWW) for supporting consensus decision making, this paper first proposes an interval composite scale based 2-tuple linguistic model, which realizes the process of translation from word to interval numerical and the process of retranslation from interval numerical to word. Second, this paper proposes an interval composite scale based personalized individual semantics model (ICS-PISM), which can provide different linguistic representation models for different decision-makers. Finally, this paper proposes a consensus decision making model with ICS-PISM, which includes a semantic translation and retranslation phase during decision process and determines the consensus state of the whole decision process. These models proposed take into full consideration that human language contains vague expressions and usually real-world preferences are uncertain, and provide efficient computation models to support consensus decision making.

This paper presents an ontology-based driving decision making system, which can promptly make safety decisions in real-world driving. Analyzing sensor data for improving autonomous driving safety has become one of the most promising issues in the autonomous vehicles research field. However, representing the sensor data in a machine understandable format for further knowledge processing still remains a challenging problem. In this paper, we introduce ontologies designed for autonomous vehicles and ontology-based knowledge base, which are used for representing knowledge of maps, driving paths, and perceived driving environments. Advanced Driver Assistance Systems (ADAS) are developed to improve safety of autonomous vehicles by accessing to the ontology-based knowledge base. The ontologies can be reused and extended for constructing knowledge base for autonomous vehicles as well as for implementing different types of ADAS such as decision making system.

Nature-inspired devices and architectures are attracting considerable attention for various purposes, including the development of novel computing techniques based on spatiotemporal dynamics, exploiting stochastic processes for computing, and reducing energy dissipation. This paper demonstrates that networks of optical energy transfers between quantum nanostructures mediated by optical near-field interactions occurring at scales far below the wavelength of light could be utilized for solving a constraint satisfaction problem (CSP), the satisfiability problem (SAT), and a decision making problem. The optical energy transfer from smaller quantum dots to larger ones, which is a quantum stochastic process, depends on the existence of resonant energy levels between the quantum dots or a state-filling effect occurring at the larger quantum dots. Such a spatiotemporal mechanism yields different evolutions of energy transfer patterns in multi-quantum-dot systems. We numerically demonstrate that networks of optical energy transfers can be used for solution searching and decision making. We consider that such an approach paves the way to a novel physical informatics in which both coherent and dissipative processes are exploited, with low energy consumption.

Several papers have presented measured function to handle multi-criteria fuzzy decision-making problems based on interval-valued intuitionistic fuzzy sets. However, in some cases, the proposed function cannot give sufficient information about alternatives. Consequently, in this paper, we will overcome previous insufficient problem and provide a novel accuracy function to measure the degree of the interval-valued intuitionistic fuzzy information. And a practical example has been provided to demonstrate our proposed approach. In addition, to make computing and ranking results easier and to increase the recruiting productivity, a computer-based interface system has been developed for decision makers to make decisions more efficiently.

During software requirements analysis, developers and stakeholders have many alternatives of requirements to be achieved and should make decisions to select an alternative out of them. There are two significant points to be considered for supporting these decision making processes in requirements analysis; 1) dependencies among alternatives and 2) evaluation based on multi-criteria and their trade-off. This paper proposes the technique to address the above two issues by using an extended version of goal-oriented analysis. In goal-oriented analysis, elicited goals and their dependencies are represented with an AND-OR acyclic directed graph. We use this technique to model the dependencies of the alternatives. Furthermore we associate attribute values and their propagation rules with nodes and edges in a goal graph in order to evaluate the alternatives with them. The attributes and their calculation rules greatly depend on the characteristics of a development project. Thus, in our approach, we select and use the attributes and their rules that can be appropriate for the project. TOPSIS method is adopted to show alternatives and their resulting attribute values.

Software agents are knowledgeable, autonomous, situated and interactive software entities. Agents' interactions are of special importance when a group of agents interact with each other to solve a problem that is beyond the capability and knowledge of each individual. Efficiency, performance and overall quality of the multi-agent applications depend mainly on how the agents interact with each other effectively. In this paper, we suggest an agent model by which we can clearly distinguish different agent's interaction scenarios. The model has five attributes: goal, control, interface, identity and knowledge base. Using the model, we analyze and describe possible scenarios; devise the appropriate reasoning and decision making techniques for each scenario; and build a library of reasoning and decision making modules that can be used readily in the design and implementation of multiagent systems.

In this paper, we propose a fuzzy Petri net model for a rule-based decision making system which contains uncertain conditions and vague rules. Using the transformation method introduced in the paper, one can obtain the fuzzy Petri net of the rule-based system. Since the fuzzy Petri net can be represented by some matrices, the algebraic form of a state equation of the fuzzy Petri net is systematically derived. Both forward and backward reasoning are performed by using the state equations. Since the proposed reasoning methods require only simple arithmetic operations under a parallel rule firing scheme, it is possible to perform real-time decision making with applications to control systems and diagnostic systems. The methodology presented is also applicable to classical (nonfuzzy) knowledge base systems if the nonfuzzy system is considered as a special case of a fuzzy system with truth values being equal to the extreme values only. Finally, an illustrative example of a rule-based decision making system is given for automobile engine diagnosis.