Proactive Decision Support System Research Articles

Understanding Human Decision Processes: Inferring Decision Strategies From Behavioral Data

This work investigates a method to infer and classify decision strategies from human behavior, with the goal of improving human-agent team performance by providing AI-based decision support systems with knowledge about their human teammate. First, an experiment was designed to mimic a realistic emergency preparedness scenario in which the test participants were tasked with allocating resources into 1 of 100 possible locations based on a variety of dynamic visual heat maps. Simple participant behavioral data, such as the frequency and duration of information access, were recorded in real time for each participant. The data were examined using a partial least squares regression to identify the participants’ likely decision strategy, that is, which heat maps they relied upon the most. The behavioral data were then used to train a random forest classifier, which was shown to be highly accurate in classifying the decision strategy of new participants. This approach presents an opportunity to give AI systems the ability to accurately model the human decision-making process in real time, enabling the creation of proactive decision support systems and improving overall human-agent teaming.

Coordinating truck appointments with container relocations and retrievals in container terminals under partial appointments information

This paper introduces a new scheduling approach that optimizes truck appointments, truck service order, and container relocations at the yard through a bi-objective IP model with a lexicographical objective function. The proposed approach serves as a proactive decision support system that coordinates truck appointments with container handling operations to avoid excessive delays caused by container relocations. The performance of the approach is compared to existing practices of the online container relocation using a case study involving an operational Japanese container terminal. A rule-based heuristic for the online container relocation problem is formulated and compared to our proposed optimization approach. Taking into account a number of essential practical aspects, including trucking company preferences, container delivery schedules, yard capacity, and appointment levels, our proposed optimization approach has the potential to significantly reduce the number of relocations in the yard while making minimal shifts in the preferred container pickup times.

A proactive decision support system for reviewer recommendation in academia

Peer review is an essential part of scientific communications to ensure the quality of publications and a healthy scientific evaluation process. Assigning appropriate reviewers poses a great challenge for program chairs and journal editors for many reasons, including relevance, fair judgment, no conflict of interest, and qualified reviewers in terms of scientific impact. With a steady increase in the number of research domains, scholarly venues, researchers, and papers in academia, manually selecting and accessing adequate reviewers is becoming a tedious and time-consuming task. Traditional approaches for reviewer selection mainly focus on the matching of research relevance by keywords or disciplines. However, in real-world systems, various factors are often needed to be considered. Therefore, we propose a multilayered approach integrating Topic Network, Citation Network, and Reviewer Network into a reviewer Recommender System (TCRRec). We explore various aspects, including relevance between reviewer candidates and submission, authority, expertise, diversity, and conflict of interest and integrate them into the proposed framework TCRRec. The paper also addresses cold start issues for researchers having unique areas of interest or for isolated researchers. Experiments based on the NIPS and AMiner dataset demonstrate that the proposed TCRRec outperforms state-of-the-art recommendation techniques in terms of standard metrics of precision@k, MRR, nDCG@k, authority, expertise, diversity, and coverage.

On the methods of qualitative estimation of the safety state of structurally complex systems

Abstract. Aim. To show a method of overcoming the uncertainty in the requirements for the quality of data in non-standard situations and ways of formalizing the decision-making process aimed at ensuring safe operation of structurally complex systems. The paper proposes a method of axiomatic construction of integrated indicators that describe the properties of a system and its operational environment through the synthesis of the risk function. Methods . Methods of system analysis of the objective, Russman’s methods of the difficulty in achieving the objectives and the Shewhart charts theory. Results. The author proposed methods of qualitative estimation of two types of safety state, i.e. “better than” (for the purpose of defining a certain target level that characterizes the safety state that is to be ideally achieved) or “not worse than” (for the purpose of defining a certain maximum allowable level that characterizes the safety state, below which it is not allowed to go), that imply certain ranges of deviation from the specified target or, respectively, the minimal allowable levels, within which the safety state evaluated with an integrated index is deemed to be acceptable. Conclusions . It is shown that, in respect to problems of safety and risk assessment of structurally complex systems, one should not try to work with specific safety-related events only. All such events are characterized by a set of properties and contributing factors with associated characteristics. One should try to identify each property and each characteristic of such property, which would later allow defining proactive and reactive control actions in response to changes in such characteristics and properties. Having worked out a property of a situation or an event, we work out a property of a risk, and it is of no significance in which specific risk this property manifests itself. Combinations of risk properties can be extremely numerous, therefore it is very difficult to predict specific situations. That causes the requirement for a proactive decision support system that ensures high-quality managerial decisions short before a critical event.

A proactive decision support system for predicting traffic crash events: A critical analysis of imbalanced class distribution

Real-time crash prediction plays a key role in enhancing traffic safety as well as mitigating disruptions to road users. The further improvements of predictability require the systemic analysis of crash likelihood within the driver–vehicle–environment triptych. This study presents a proactive decision support system that can predict crash events based on vehicle kinematics, driver inputs, roadway geometric features and real-time weather data. Modeling approaches that rely on Random forest, Support Vector Machine and Multilayer Perceptron machine learning techniques were applied to establish efficient crash predictions. Moreover, crash events are generally unexpected and occur rarely, thus classification results can yield deceivingly high prediction performance which are usually driven by the majority class at the expense of having poor performance on the crucial minority class. Therefore, this paper attempts to add to the current knowledge by investigating crash likelihood based on compared different data balancing techniques to improve the predictive performance through three balancing techniques: over-sampling, under-sampling and synthetic minority over-sampling (SMOTE). The highest performances have been acquired using SMOTE strategy as MLP achieved a 94.5% precision, 94.2% f1-score, 93.7% AUC and 95.3% recall, while SVM achieved a 91.5% g-mean. Furthermore, results indicated that more than 62% of total crashes have been reported in downhills and curved downhills, and 44% of all crash instances have been reported during both snow and rain weather patterns. Overall, the findings highlighted the significance of the explanatory variables associated with potential crash events and can suggest to decision-makers a safe and credible system for enhancing traffic safety.

RADAR: automated task planning for proactive decision support

ABSTRACT Proactive Decision Support aims at improving the decision making experience of human decision-makers by enhancing the quality of the decisions and the ease of making them. Given that AI techniques are efficient in searching over a potentially large solution space (of decision) and finding good solutions, it can be used for human-in-the-loop scenarios such as disaster response that demand naturalistic decision making. A human decision-maker, in such scenarios, may experience high-cognitive overload leading to a loss of situational awareness. In this paper, we propose the use of automated task-planning techniques coupled with design principles laid out in the Human-Computer Interaction (HCI) community for developing a proactive decision support system. To this extent, we highlight the capabilities of such a system RADAR and briefly, describe how automated planning techniques help us in providing the varying degrees of assistance. To evaluate the effectiveness of the different capabilities, we conduct ablation studies with human subjects on a synthetic environment for making an interactive plan of study. We found that planning techniques like plan validation and suggestions help to reduce planning time (objective metrics) and improves user satisfaction (subjective metrics) compared to expert human planners without any support.

Context‐Driven Proactive Decision Support for Hybrid Teams

A synergy between AI and the Internet of Things (IoT) will significantly improve sense‐making, situational awareness, proactivity, and collaboration. However, the key challenge is to identify the underlying context within which humans interact with smart machines. Knowledge of the context facilitates proactive allocation among members of a human‐smart machine (agent) collective that balances autonomy with human interaction, without displacing humans from their supervisory role of ensuring that the system goals are achievable. In this article, we address four research questions as a means of advancing toward proactive autonomy: how to represent the interdependencies among the key elements of a hybrid team; how to rapidly identify and characterize critical contextual elements that require adaptation over time; how to allocate system tasks among machines and agents for superior performance; and how to enhance the performance of machine counterparts to provide intelligent and proactive courses of action while considering the cognitive states of human operators. The answers to these four questions help us to illustrate the integration of AI and IoT applied to the maritime domain, where we define context as an evolving multidimensional feature space for heterogeneous search, routing, and resource allocation in uncertain environments via proactive decision support systems.

A Proactive Decision Support System for Online Event Streams

In-stream big data processing is an important part of big data processing. Proactive decision support systems can predict future system states and execute some actions to avoid unwanted states. In this paper, we propose a proactive decision support system for online event streams. Based on Complex Event Processing (CEP) technology, this method uses structure varying dynamic Bayesian network to predict future events and system states. Different Bayesian network structures are learned and used according to different event context. A networked distributed Markov decision processes model with predicting states is proposed as sequential decision making model. A Q-learning method is investigated for this model to find optimal joint policy. The experimental evaluations show that this method works well for congestion control in transportation system.

A proactive decision support method based on deep reinforcement learning and state partition

Big streaming data is an important kind of big data which we need new technology to process. Getting knowledge from online streaming data and making decision online can help us get more value from Big data. A proactive decision support system can predict future states and mitigate or eliminate undesired future states by taking some actions proactively. But it is difficult to handle some issues like the data distribution change in streaming data, combination of prediction and decision making, and the huge state space in decision making. In this paper, we propose a proactive decision support method based on deep reinforcement learning and state partition. The predictive analytics part uses deep belief networks with two level incremental training method. The deep reinforcement learning part uses deep belief networks as function approximation which is learned by semi-gradient method. Off-policy is supported through important sampling. Two kinds of state partition and parallel execution methods are proposed to improve the performance. The experimental evaluation in traffic congestion control application shows this method works well in both accuracy and performance.

Spatiotemporal Traffic State Prediction Based on Discriminatively Pre-trained Deep Neural Networks

The availability of traffic data and computational advances now make it possible to build data-driven models that capture the evolution of the state of traffic along modeled stretches of road. These models are used for short-time prediction so that transportation facilities can be operated in an efficient way that guarantees a high level of service. In this paper, we adopted a state-of-the-art machine learning deep neural network and the divide-and-conquer approach to model large road stretches. The proposed approach is expected to be a tool used in daily routines to enhance proactive decision support systems. The proposed approach maintains spatiotemporal correlations between contiguous road segments and is suitable for practical applications because it divides the large prediction problem into a set of smaller overlapping problems. These smaller problems can be solved in a reasonable time using a medium configuration PC. The proposed approach was used to model 21.1- and 30.7-mile stretches of highway along I-15 and I-66, respectively. The resulting predictions were better than predictions obtained using partial least squares regression.

Outlier detection and classification in sensor data streams for proactive decision support systems

A paper has a deal with the problem of quality assessment in sensor data streams accumulated by proactive decision support systems. The new problem is stated where outliers need to be detected and to be classified according to their nature of origin. There are two types of outliers defined; the first type is about misoperations of a system and the second type is caused by changes in the observed system behavior due to inner and external influences. The proposed method is based on the data-driven forecast approach to predict the values in the incoming data stream at the expected time. This method includes the forecasting model and the clustering model. The forecasting model predicts a value in the incoming data stream at the expected time to find the deviation between a real observed value and a predicted one. The clustering method is used for taxonomic classification of outliers. Constructive neural networks models (CoNNS) and evolving connectionists systems (ECS) are used for prediction of sensors data. There are two real world tasks are used as case studies. The maximal values of accuracy are 0.992 and 0.974, and F1 scores are 0.967 and 0.938, respectively, for the first and the second tasks. The conclusion contains findings how to apply the proposed method in proactive decision support systems.

A proactive decision making framework for condition-based maintenance

Purpose – The purpose of this paper is to perform an extensive literature review in the area of decision making for condition-based maintenance (CBM) and identify possibilities for proactive online recommendations by considering real-time sensor data. Based on these, the paper aims at proposing a framework for proactive decision making in the context of CBM. Design/methodology/approach – Starting with the manufacturing challenges and the main principles of maintenance, the paper reviews the main frameworks and concepts regarding CBM that have been proposed in the literature. Moreover, the terms of e-maintenance, proactivity and decision making are analysed and their potential relevance to CBM is identified. Then, an extensive literature review of methods and techniques for the various steps of CBM is provided, especially for prognosis and decision support. Based on these, limitations and gaps are identified and a framework for proactive decision making in the context of CBM is proposed. Findings – In the proposed framework for proactive decision making, the CBM concept is enriched in the sense that it is structured into two components: the information space and the decision space. Moreover, it is extended in a way that decision space is further analyzed according to the types of recommendations that can be provided. Moreover, possible inputs and outputs of each step are identified. Practical implications – The paper provides a framework for CBM representing the steps that need to be followed for proactive recommendations as well as the types of recommendations that can be given. The framework can be used by maintenance management of a company in order to conduct CBM by utilizing real-time sensor data depending on the type of decision required. Originality/value – The results of the work presented in this paper form the basis for the development and implementation of proactive Decision Support System (DSS) in the context of maintenance.

Neither snow nor rain: contingency planning by a clinical reference laboratory courier service for weather related emergencies.

To optimize transportation processes, we present herein a contingency plan that coordinates interim measures used to ensure continued and timely services when climate based events might cause an interruption of the usual specimen transportation processes. As an example, we outline the implementation and effectiveness of a contingency plan for network laboratory courier automobile transportation during times of mountain pass highway closure. Data available from an approximately 3-year period from October 10, 2010 through August 29, 2013 revealed a total of 690 complete closures in the eastbound or westbound lanes of the Interstate-90 highway in the Snoqualmie Pass area in the state of Washington. Despite the frequency of closures, the Washington State Department of Transportation was effective in limiting the duration of closures. Road closures of less than 1 hour accounted for 58.7% of the total closures. No recorded closures prevented dispatched couriers from completing a prescheduled Snoqualmie Pass route. We identified no delays as being clinically significant, despite that there were 5 instances of delays greater than 4 hours. We implemented a contingency plan of aiding courier logistics during all times of pass closure. The plan includes an easy to interpret Condition Dashboard as a status indicator and a Decision Tree that references and summarizes information. Overall, the contingency plan allows for an objective, robust, proactive decision support system that has enabled operational flexibility and has contributed to continued safe, on-time specimen transportation; clients and courier and reference laboratory staff have appreciated these features and associated outcomes.

UbiDSS: a proactive intelligent decision support system as an expert system deploying ubiquitous computing technologies

Abstract Many industries have deployed expert systems (ESs) to provide support for solving complicated and specialized problems since 1980. Because ESs are originally designed for generating feasible alternatives in an automated manner, users, especially decision makers, expect ESs to make decisions proactively and intelligently by automatically detecting users' contextual data. In other words, decision makers require decision support systems (DSSs) to be reinforced with proactive and intelligent ESs that can utilize contextual data. Meanwhile, ubiquitous technology-based applications currently only provide limited personalization services that use the user's context and preferences; in other words, these systems do not fully make use of sophisticated decision making capabilities. Hence, this paper describes how decision making and context-aware computing are jointly used to establish ubiquitous computing technology-based applications. To do so, we propose an amended DSS paradigm, Context-Knowledge-Dialogue-Data-Model (CKDDM). This model describes what may be considered for future DSSs when we regard ubiquitous computing technology as an inevitable effect that changes how decision making decisions are described. Under the CKDDM paradigm, a framework of ubiquitous decision support systems (ubiDSS) is addressed with the description of the subsystems within. To show the feasibility of ubiDSS, a prototype system, Context-Aware Multi Agent System-My Optimization (CAMA-myOpt) has been implemented as an illustrative example system.