Many human-computer interactions and automated processes are dependent on data. We need to correctly store data and retrieve data to support these processes. Large Language Models (LLMs) have made significant progress in comprehension and reasoning. However, we have found that their ability to handle professional data is weak, which significantly limits their applications in automated processes and professional applications. These scenarios often involve a large number of similar data entries, which are challenging for LLMs such as ChatGPT-4 Omni. In this paper, we propose a Rational Intelligence Model that comprehends human experts' knowledge of data structure and process requirements of data, automatically extracts data from conversations with end users, and effectively stores the data and retrieves it for supporting the interaction and process. Experiments show that ChatGPT-4o can achieve 0% error in simple queries, 2.6% errors when data entries are out of order, and 38% errors when the queries are reasonably complex. However, with our proposed Rational Intelligence Model (RIM), we can achieve 0% error rate in all tests. RIM fundamentally changes software engineering and expert system development approaches. Instead of having a software engineer understand expert knowledge of data processing, this is now achieved by RIM, which means it is much more flexible, lower in cost, and requires much less development time.

This research proposed a model called rational intelligence and studied its reasoning capability as compared to ChatGPT-4o in counterfactual reasoning tasks. Unlike traditional AI models that rely heavily on data-driven approaches, rational intelligence allows for reasoning over abstract principles and hypothetical scenarios, similar to human cognitive processes. The proposed Rational Intelligence Model (RIM) applies Large Language Models (LLMs) to enable human-like comprehension, knowledge application, and problem-solving capabilities. In complex counterfactual reasoning tasks with scenarios proven to be challenging to human adults, we demonstrate that RIM achieves a clearly higher accuracy rate (76%) compared to ChatGPT-4o (68%), showing its enhanced reasoning capabilities. Additionally, RIM incorporates a self-reflection mechanism to manage knowledge conflicts and gaps, which can further improve its performance and adaptability.

Mobile edge computing (MEC) provides a new distributed computing paradigm that overcomes the inability of cloud computing to offer low end-to-end latency. In a MEC environment, app vendors can deliver lower-latency services to mobile app users by placing applications on edge servers in close proximity to app users. From an app vendor's perspective, an optimal edge application placement strategy under a budget ($k$) constraint aims to place application instances on$k$edge servers within a specific area to maximize user coverage. However, edge servers may be subject to failure due to multiple reasons, e.g., hardware faults, software exceptions, cyber-attacks, etc. App users served by failed edge servers need to access applications from remote cloud servers if they cannot access any other edge servers. This impacts app users’ quality of experience significantly. Thus, app vendors need to consider the reliability of the edge server network when choosing edge servers for placing their application instances. We make the first attempt in this paper to tackle this problem of joint optimization of coverage and reliability for edge application placement (EAP-CR). We formulate this problem as a constrained optimization problem and prove its$\mathcal {NP}$-hardness theoretically. Then, we propose an optimal approach to find the optimal solutions with the integer programming technique, an approximation approach is also proposed to find approximate solutions for large-scale EAP-CR problems. We evaluate EAP-$OPT$and EAP-$APX$against three relevant approaches through experiments conducted on a widely-used real-world data set and a synthetic data set. The results demonstrate that our proposed approaches can solve the EAP-CR problem effectively and efficiently.

Online health information services and self diagnosis systems become popular recent years. We propose a computing model for collaborative medical diagnosis through multi agent argumentation. In this model, the agents are able to communicate with each other to share information, critique and verify each other's knowledge, and collaboratively make diagnosis based on multiple agents' knowledge through an argumentation process. Fuzzy Petri Net (FPN) is adopted as the agents' knowledge model. Different from the commonly used FPNs that assign tokens in places, we assign tokens on arcs and also give places capability in controlling the inference of FPN. The FPN based argumentation is automated with algorithms. The proposed model can be employed to achieve collaborative healthcare diagnosis systems, where agents with different expertise collaboratively argue with each other to come up with a mutually agreed diagnosis.

Argumentation plays an important role in promoting deep learning, fostering conceptual change and supporting problem solving. The new “learning by arguing” paradigm leads to new learning opportunities. However, due to the difficulties in modeling human cognition, there are few learning systems that can facilitate argumentation dialogues between systems and learners. Fuzzy Cognitive Map (FCM) is an effective tool in modeling human cognition. This paper proposes an FCM based argumentation model. Based on this model we design an argumentative software agent to facilitate argumentative learning. Provided with the domain knowledge and argumentation capability, the agent is able to simulate a peer learner and automatically conduct argumentative dialogues with learners. The argumentative agent can be applied in general school education as well as special domains like diabetes education and eHealth decision support.

Riding on the back of the rapid expansion of the Internet, online virtual worlds which combine the prowess of interactive digital media and social networks have attained a high level of acceptance among the Net Generation users. This development prompted researchers to look into the potential of embedding learning contents into virtual worlds to create virtual learning environments (VLEs) that suit the need of the Net Generation learners. However, the special characteristics of virtual worlds that make them popular also pose great challenges to educators who wish to leverage their power. These challenges call for more sophisticated human computer interaction (HCI) mechanisms to assist learners to navigate the intriguing landscape of VLEs. In this paper, we demonstrate a teachable remembrance agent which acts as an intelligent user interface to provide innovative ways for students to interact with VLEs.

Cognitive maps (CMs), fuzzy cognitive maps (FCMs), and dynamical cognitive networks (DCNs) are related tools for modeling the cognition of human beings and facilitating machine inferences accordingly. FCMs extend CMs, and DCNs extend FCMs. Domain experts often face the challenge that CMs/FCMs are not sufficiently capable in many applications and that DCNs are too complex. This paper presents a simplified DCN (sDCN) that extends the modeling capability of FCM/CM, yet maintains simplicity. Additionally, this paper proves that there exists a theoretical equivalence among models in the cognitive map family of CMs, FCMs, and sDCNs. It shows that every sDCN can be represented by an FCM or a CM, andvice versa; similarly, every FCM can be represented by a CM, andvice versa. The result shows that CMs, FCMs, and sDCNs are a family of cognitive models that differs from many extended models. This paper also provides a constructive approach to transforming one cognitive map model into other cognitive map models in the family. Therefore, domain experts are able to model applications with more descriptive sDCNs and leave theoretical analysis to the simpler CM forms. The existence of theoretical transformation links among the models provides strong support for their theoretical analysis and flexibility in their applications.

Learning activity negotiation will involve learner's commitment, engage the learner and improve learning quality. However, in current learning systems, learning activities are either decided by the system or decided by the learner. This paper proposes an intelligent negotiation mechanism, where the system and the learner can discuss about their needs and negotiate on learning activities. Because the learner and the system all participate in the decision making, the learning activities selected may meet both the learnerpsilas learning style and experience, as well as the system's curriculum requirement.

Cognitive map (CM), fuzzy cognitive map (FCM) are two related tools for modeling human being's cognition and facilitating machine deduction over the cognitive model accordingly. FCM has extended CM by modeling the strength of the causal relationship. To acquire the ability of modeling the strength of cause and effect, multi-value FCM (MVFCM) is formally presented in this paper, which uses mutli-value concepts. Not surprisingly, FCM is more complex than CM and MVFCM is more complex than FCM. This paper proves that there exists a theoretical equivalence among MVFCM, FCM and CM. It shows that for every MVFCM, there exists a FCM, or a CM that represents the MVFCM. This result allows domain experts to model applications with more descriptive MVFCM form and perform theoretical analysis with the simpler CM form. The equivalence among the models also provides strong support to the theoretical analysis of the models and flexibility to their applications.

Goal selection and action selection are important topics in agent research. This paper first describes an agent model, Goal Net, which supports multiple goal selection methods and action selection mechanisms for an agent. Then a goal selection algorithm and action selection mechanisms supported by Goal Net are presented. Examples from real system development are also given to illustrate the algorithm and mechanisms.

Fuzzy Cognitive Map (FCM) is a powerful and flexible framework for knowledge representation and causal inference. However, in most real applications, it is difficult to design and analyze FCMs due to their structural complexity. Simplification, merging, and division are the important operations on the structure of FCMs. In this paper we present approaches to simplifying FCMs. These approaches show how to clean up a FCM, how to divide a complex FCM into basic FCMs, and how to extract the eigen structure of these basic FCMs. Two improved methods for merging FCMs from different human experts are also proposed in this paper. We discuss difficulties in merging FCMs and present possible solutions.