Emotions have been subject of research and deliberation in philosophy and psychology mainstream for a long time. In contrast, emotions have only emerged in artificial intelligence research as a serious topic in the last two decades. Year 2000, in particular, experienced a shift in attitude towards emotions and their relationship to human reasoning and human–computer interaction. This shift continued slowly but surely over the years and computational emotions can be seen as a mainstream research topic within artificial intelligence and cognitive systems. In this paper, we attempt to contribute to the development of this area by interpreting psychological theories of emotions computationally and translating them into machine implementable models. These models are generic and application independent which most of the current computational emotions models lack. We have selected two psychological theories, namely Millenson (The psychology of emotion: theories of emotion perspective, vol 4. Wiley, New Jersey, pp 35–36, 1967) and Scherer (Soc Sci Inf 44(4):695–729, 2005), that lend themselves, with varying degrees of difficulty, to the computational interpretation. Fuzzy logic was utilised as a tool to keep the fidelity of psychological interpretation of emotion. The paper discusses in details the computational interpretation of these psychological models and presents a full theoretical formalism in fuzzy logic type 1, implementation and detailed analysis of this psychologically grounded generic computational models.

Models for Computational Emotions from Psychological Theories Using Type-II Fuzzy Logic

The present discussion highlights the aspects of an ongoing doctoral thesis grounded on the analysis and tracking of emotions from English and Bengali texts. Development of lexical resources and corpora meets the preliminary urgencies. The research spectrum aims to identify the evaluative emotional expressions at word, phrase, sentence, and document level granularities along with their associated holders and topics. Tracking of emotions based on topic or event was carried out by employing sense based affect scoring techniques. The labeled emotion corpora are being prepared from unlabeled examples to cope with the scarcity of emotional resources, especially for the resource constraint language like Bengali. Different unsupervised, supervised and semi-supervised strategies, adopted for coloring each outline of the research spectrum produce satisfactory outcomes

/pdf/analysis-and-tracking-of-emotions-in-english-and-bengali-1uvbsjn91r.pdf

Analysis and tracking of emotions in english and bengali texts: a computational approach

Personality and emotions are effective factors in human decision-making processes. Thus, when an agent has to emulate human behaviour, not only should this agent think and reason but also should have emotions and personality. In this article a fuzzy agent with dynamic personality is modelled based on a five-factor personality model and implemented in a Java environment. Then it is extended with a fuzzy emotion component. This emotion component uses calculated personality factors and some related parameters and then determines the degree of anger. The proposed personality and emotion model provides a proper framework for human-like agent decision-making tasks.

Anger evaluation for fuzzy agents with dynamic personality

Human-like holder plays an important role in identifying actual emotion expressed in text. This paper presents a baseline followed by syntactic approach for capturing emotion holders in the emotional sentences. The emotional verbs collected from WordNet Affect List (WAL) have been used in extracting the holder annotated emotional sentences from VerbNet. The baseline model is developed based on the subject information of the dependency-parsed emotional sentences. The unsupervised syntax based model is based on the relationship of the emotional verbs with their argument structure extracted from the head information of the chunks in the parsed sentences. Comparing the system extracted argument structure with available VerbNet frames' syntax for 942 emotional verbs, it has been observed that the model based on syntax outperforms the baseline model. The precision, recall and F-Score values for the baseline model are 63.21%, 66.54% and 64.83% and for the syntax based model are 68.11%, 65.89% and 66.98% respectively on a collection of 4,112 emotional sentences.

/pdf/emotion-holder-for-emotional-verbs-the-role-of-subject-and-3u85ujc0x2.pdf

Emotion holder for emotional verbs – the role of subject and syntax

—Efficient spectrum decision and spectrum mobility schemes for cognitive radio based inter vehicle communication (IVC) system have been anticipated in this research. Cognitive Radio has four main functions, spectrum sensing, spectrum management, spectrum mobility and spectrum sharing. At high speed of vehicles performing spectrum decision and spectrum mobility is not an easy task for cognitive radio. Spectrum mobility is very frequent in IVC due to extremely mobile nature of vehicles and unpredictable radio frequency channel. A proficient white space optimization technique is a basic requirement to perform in time spectrum decision and spectrum mobility. Genetic Algorithm (GA) is considered as one of the best optimization techniques for white space optimization. But at high vehicular speed simple genetic algorithm (GA) has been botched to perform optimization. To anticipate this problem, we have already proposed Memory Enabled Genetic algorithm (MEGA) in our previous work. In this research work we have further improved the convergence time of MEGA by manipulating the generation gap, crossover and mutation operator of MEGA. Simulation results proved that improved memory enabled genetic algorithm (IMEGA) is 0.522 ms faster than MEGA. Simulation results revealed that using our proposed spectrum decision and spectrum mobility schemes are more efficient cognitive radio based vehicular networks.

/pdf/efficient-decision-based-spectrum-mobility-scheme-for-4abhx0cr5r.pdf

Efficient Decision Based Spectrum Mobility Scheme for Cognitive Radio Based V2V Communication System

The research result of neurobiology shows that emotions have a great effect on the intelligence of human beings. To make agent have the ability of handling emotions, a new model structure of emotion agent is proposed, based on the traditional BDI agent model. The model firstly establishes a new emotion knowledge base for emotion expression, secondly performs emotion reasoning by an emotional reasoning algorithm, and finally implements the emotions treatment. An emotional agent based on this model has been realized, experiment results show that it is efficient to transact simple emotions.

Model of emotional agent

The importance of emotion has been identified in human-like intelligence recently. To improve the ability of agent for handling emotions, a new architecture for emotion agent is presented, based on the traditional BDI agent model. The architecture firstly establishes a new emotion knowledge base for emotion expression, secondly performs emotion reasoning by an emotional reasoning algorithm, and finally implements the emotion treatment. An emotional agent grid based on this architecture has been realized, experiment results show that it is efficient to transact simple emotions by emotional agents.

An Architecture for Emotional Agent

This paper firstly introduces quantum computing theory into the study of granular computing, then puts forward a new granular computing model based on quantum computing theory, which fully makes full use of the parallelism of quantum computing and layered characteristics of the granular computing. As Grover quantum algorithm has some shortcomings in the classical target number of solutions to solve more problems in the search space, this paper further puts forward an improved Grover quantum search algorithm, and experiment results show it is effective.

Granular Computing Model Based on Quantum Computing Theory

Research results of neurobiology show that emotions play an important and active role in human-like intelligence. To make agent have the ability of handling emotions, a new model structure of emotional agent is presented, based on the granular computing theory and the traditional BDI agent model. The model firstly establishes a new emotion knowledge base based on granular computing for emotion expression, secondly puts forward the emotional decision rules achieving method of the emotional Knowledge base, and finally implements the emotion treatment. An emotional agent based on this model has been realized, experiment results show that it is efficient to transact simple emotions.

A Model of Emotional Agent Based on Granular Computing

Some actual multi-agent automated negotiation systems using auction mechanism in e-commerce are inefficient, as buyer negotiation agents are lack of enough rationality and uneasy to determine appropriate bid price automatically according to different circumstance. In order to improve the auction-based negotiation efficiency of multi-agent system in e-commerce, this paper proposes a model of game agent for auction-based negotiation and a bid negotiation algorithm based on game theory, which provide a new effective way to establish buyer game agent for making buyer agent determine price more accurately and bid more rationally. As buyer agents bid in a more rational way, auction agents and buyer agents can finish negotiation more efficiently.

Model of game agent for auction-based negotiation

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Jun Hu

Author Tools

Chat about Author