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John Yen

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18 papers
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18

JAAMAS Journal 2026 Journal Article

FLAME—Fuzzy Logic Adaptive Model of Emotions

  • Magy Seif El-Nasr
  • John Yen
  • Thomas R. Ioerger

Abstract Emotions are an important aspect of human intelligence and have been shown to play a significant role in the human decision-making process. Researchers in areas such as cognitive science, philosophy, and artificial intelligence have proposed a variety of models of emotions. Most of the previous models focus on an agent's reactive behavior, for which they often generate emotions according to static rules or pre-determined domain knowledge. However, throughout the history of research on emotions, memory and experience have been emphasized to have a major influence on the emotional process. In this paper, we propose a new computational model of emotions that can be incorporated into intelligent agents and other complex, interactive programs. The model uses a fuzzy-logic representation to map events and observations to emotional states. The model also includes several inductive learning algorithms for learning patterns of events, associations among objects, and expectations. We demonstrate empirically through a computer simulation of a pet that the adaptive components of the model are crucial to users' assessments of the believability of the agent's interactions.

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IS Journal 2014 Journal Article

Social Intelligence and Technology

  • Christopher C. Yang
  • John Yen
  • Jiming Liu

In this special issue on social intelligence and technology, the guest editors discuss social media's evolution, including societal problems that have arisen or been solved as a result of this technology. They also introduce articles that offer novel solutions in the field.

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AAMAS Conference 2007 Conference Paper

Realistic Cognitive Load Modeling for Enhancing Shared Mental Models in Human-Agent Collaboration

  • Xiaocong Fan
  • John Yen

Human team members often develop shared expectations to predict each other's needs and coordinate their behaviors. In this paper the concept "Shared Belief Map" is proposed as a basis for developing realistic shared expectations among a team of Human-Agent-Pairs (HAPs). The establishment of shared belief maps relies on inter-agent information sharing, the effectiveness of which highly depends on agents' processing loads and the instantaneous cognitive loads of their human partners. We investigate HMM-based cognitive load models to facilitate team members to "share the right information with the right party at the right time". The shared belief map concept and the cognitive/processing load models have been implemented in a cognitive agent architecture– SMMall. A series of experiments were conducted to evaluate the concept, the models, and their impacts on the evolving of shared mental models of HAP teams.

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LPAR Conference 2002 Conference Paper

A Framework for Splitting BDI Agents

  • Xiaocong Fan
  • John Yen

Abstract Agent splitting is useful in at least three fields. In mobile computing, it’s more reasonable to transfer smarter and smaller clones of an agent rather than the bulky agent itself. In agent teamwork field, it can be used as the basis for modeling the shared mental state of team-based agents. In Multi-Agent systems, it can be embedded as a built-in load-balancing mechanism. Based on a simple BDI agent model, this paper studies how to split BDI agents logically while preserving the implicit information chains.

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AAAI Conference 1996 Conference Paper

Using a Hybrid Genetic Algorithm and Fuzzy Logic for Metabolic Modeling

  • John Yen

The identification of metabolic systems is a complex task due to the complexity of the system and limited knowledge about the model. Mathematical equations and ODE’ s have been used to capture the structure of the model, and the conventional optimization techniques have been used to identify the parameters of the model. In general, however, a pure mathematical formulation of the model is difficult due to parametric uncertainty and incomplete knowledge of mechanisms. In this paper, we propose a modeling approach that (1) uses fuzzy rule-based model to augment algebraic enzyme models that are incomplete, and (2) uses a hybrid genetic algorithm to identify uncertain parameters in the model. The hybrid genetic algorithm (GA) integrates a GA with the simplex method in functional optimization to improve the GA’ s convergence rate. We have applied this approach to modeling the rate of three enzyme reactions in E. cola’ central metabolism. The proposed modeling strategy allows (1) easy incorporation of qualitative insights into a pure mathematical model and (2) adaptive identification and optimization of key parameters to fit system behaviors observed in biochemical experiments.

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AAAI Conference 1994 Conference Paper

The Acquisition, Analysis and Evaluation of Imprecise Requirements for Knowledge-Based Systems

  • John Yen

In this paper, a theoretical foundation has been laid and a practical method has been developed for specifying, analyzing and evaluating the complex relationships between imprecise requirements in knowledge-based systems. Imprecise requirements are represented by the canonical form in test-score semantics. The relationships between requirements are classified to be conflicting and cooperative based on the qualitative and quantitative analysis of relationships between requirements. This kind of analysis makes it possible to formulate a feasible overall requirement from conflicting individual requirements. It also facilitates to find better trade-off strategies for conflicting requirements by using fuzzy multicriteria optimization technique. A requirement engineering process has also been developed to incorporate imprecise requirements into the requirement analysis for knowledge-based systems. knowledge-based systems is that requirements often conflict with each other [Robinson 19901. However existing specification methods consider that a requirement specification, which contains conflicting requirements, to be inconsistent, and should be avoided since requirements are specified as crisp ones [Roman 19851. Moreover, it is very difficult to analyze and specify a trade-off between conflicting requirements if these requirements are specified to be crisp [Robinson 19901.

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AAAI Conference 1990 Conference Paper

A Principled Approach to Reasoning About the Specificity of Rules

  • John Yen

Even though specificity has been one of the most useful conflict resolution strategies for selecting productions, most existing rule-based systems use heuristic approximation such as the number of clauses to measure a rule’ s specificity. This paper describes an approach for computing a principled specificity relation between rules whose conditions are constructed using predicates defined in a terminological knowledge base. Based on a formal definition about pattern subsumption relation, we first show that a subsumption test between two conjunctive patterns can be viewed as a search problem. Then we describe an implemented pattern classification algorithm that improves the efficiency of the search process by deducing implicit conditions logically implied by a pattern and by reducing the search space using subsumption relationships between predicates. Our approach enhances the maintainability of rule-based systems and the reusability of definitional knowledge.

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AAAI Conference 1986 Conference Paper

A Reasoning Model Based on an Extended Dempster- Shafer Theory

  • John Yen

The Dempster-Shafer (D-S) theory of evidence suggests a coherent approach to aggregate evidence bearing on groups of mutually exclusive hypotheses; however, the uncertain relationships between evidence and hypotheses are difficult to represent in applications of the theory. In this paper, we extend the multivalued mapping in the D-S theory to a probabilistic one that uses conditional probabilities to express the uncertain associations. In addition, Dempster’s rule is used to combine belief update rather than absolute belief to obtain results consistent with Bayes’ theorem. The combined belief intervals form probability bounds under two conditional independence assumptions. Our model can be applied to expert systems that contain sets of mutually exclusive and exhaustive hypotheses, which may or may not form hierarchies.

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