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Artificial Intelligence and Robotics
Research Collection School Of Computing and Information Systems
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- Adaptive Resonance Theory (2)
- Forgetting (2)
- Intelligent agents (2)
- Memory (2)
- ART (1)
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- Affective model (1)
- Agent (1)
- Autonomous Learning 2012 (1)
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- INNS-WC 2012 (1)
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- Rocchio (1)
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Articles 1 - 10 of 10
Full-Text Articles in Databases and Information Systems
Investigating Intelligent Agents In A 3d Virtual World, Yilin Kang, Fiona Fui-Hoon Nah, Ah-Hwee Tan
Investigating Intelligent Agents In A 3d Virtual World, Yilin Kang, Fiona Fui-Hoon Nah, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Web 3.0 involves "intelligent" web applications that utilize natural language processing, machine-based learning and reasoning, and intelligent techniques to analyze and understand user behavior. In this research, we empirically assess a specific form of Web 3.0 application in the form of intelligent agents that offer assistance to users in the virtual world. Using media naturalness theory, we hypothesize that the use of intelligent agents in the virtual world can enhance user experience by offering a more natural way of communication and assistance to users. We are interested to test if media naturalness theory holds in the context of intelligent agents …
Knowledge-Based Exploration For Reinforcement Learning In Self-Organizing Neural Networks, Teck-Hou Teng, Ah-Hwee Tan
Knowledge-Based Exploration For Reinforcement Learning In Self-Organizing Neural Networks, Teck-Hou Teng, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Exploration is necessary during reinforcement learning to discover new solutions in a given problem space. Most reinforcement learning systems, however, adopt a simple strategy, by randomly selecting an action among all the available actions. This paper proposes a novel exploration strategy, known as Knowledge-based Exploration, for guiding the exploration of a family of self-organizing neural networks in reinforcement learning. Specifically, exploration is directed towards unexplored and favorable action choices while steering away from those negative action choices that are likely to fail. This is achieved by using the learned knowledge of the agent to identify prior action choices leading to …
Agent-Based Virtual Humans In Co-Space: An Evaluative Study, Yilin Kang, Ah-Hwee Tan, Fiona Fui-Hoon Nah
Agent-Based Virtual Humans In Co-Space: An Evaluative Study, Yilin Kang, Ah-Hwee Tan, Fiona Fui-Hoon Nah
Research Collection School Of Computing and Information Systems
Co-Space refers to interactive virtual environment modelled after the real world in terms of look-and-feel, functionalities and services. We have developed a 3D virtual world named Nan yang Technological University (NTU) Co-Space populated with virtual human characters. Three key requirements of realistic virtual humans in the virtual world have been identified, namely (1) autonomy: agents can function on their own, (2) interactivity: agents can interact naturally with players, and (3) personality: agents can exhibit human traits and characteristics. Working towards these challenges, we propose a brain-inspired agent architecture that integrates goal-directed autonomy, natural language interaction and human-like personality. We conducted …
A Generalized Cluster Centroid Based Classifier For Text Categorization, Guansong Pang, Shengyi Jiang
A Generalized Cluster Centroid Based Classifier For Text Categorization, Guansong Pang, Shengyi Jiang
Research Collection School Of Computing and Information Systems
In this paper, a Generalized Cluster Centroid based Classifier (GCCC) and its variants for text categorization are proposed by utilizing a clustering algorithm to integrate two wellknown classifiers, i.e., the K-nearest-neighbor (KNN) classifier and the Rocchio classifier. KNN, a lazy learning method, suffers from inefficiency in online categorization while achieving remarkable effectiveness. Rocchio, which has efficient categorization performance, fails to obtain an expressive categorization model due to its inherent linear separability assumption. Our proposed method mainly focuses on two points: one point is that we use a clustering algorithm to strengthen the expressiveness of the Rocchio model; another one is …
Cognitive Architectures And Autonomy: Commentary And Response, Włodzisław Duch, Ah-Hwee Tan, Stan Franklin
Cognitive Architectures And Autonomy: Commentary And Response, Włodzisław Duch, Ah-Hwee Tan, Stan Franklin
Research Collection School Of Computing and Information Systems
This paper provides a very useful and promising analysis and comparison of current architectures of autonomous intelligent systems acting in real time and specific contexts, with all their constraints. The chosen issue of Cognitive Architectures and Autonomy is really a challenge for AI current projects and future research. I appreciate and endorse not only that challenge but many specific choices and claims; in particular: (i) that “autonomy” is a key concept for general intelligent systems; (ii) that “a core issue in cognitive architecture is the integration of cognitive processes ....”; (iii) the analysis of features and capabilities missing in current …
Memory Formation, Consolidation, And Forgetting In Learning Agents, Budhitama Susnagdja, Wenwen Wang, Ah-Hwee Tan, Yuan-Sin Tan, Loo-Nin Teow
Memory Formation, Consolidation, And Forgetting In Learning Agents, Budhitama Susnagdja, Wenwen Wang, Ah-Hwee Tan, Yuan-Sin Tan, Loo-Nin Teow
Research Collection School Of Computing and Information Systems
Memory enables past experiences to be remembered and acquired as useful knowledge to support decision making, especially when perception and computational resources are limited. This paper presents a neuropsychological- inspired dual memory model for agents, consisting of an episodic memory that records the agent's experience in real time and a semantic memory that captures factual knowledge through a parallel consolidation process. In addition, the model incorporates a natural forgetting mechanism that prevents memory overloading by removing transient memory traces. Our experimental study based on a real-time first-person-shooter video game has indicated that the memory consolidation and forgetting processes are not …
Memory Formation, Consolidation, And Forgetting In Learning Agents, Budhitama Subagdja, Wenwen Wang, Ah-Hwee Tan, Yuan-Sin Tan, Loo-Nin Teow
Memory Formation, Consolidation, And Forgetting In Learning Agents, Budhitama Subagdja, Wenwen Wang, Ah-Hwee Tan, Yuan-Sin Tan, Loo-Nin Teow
Research Collection School Of Computing and Information Systems
Memory enables past experiences to be remembered and acquired as useful knowledge to support decision making, especially when perception and computational resources are limited. This paper presents a neuropsychological-inspired dual memory model for agents, consisting of an episodic memory that records the agent’s experience in real time and a semantic memory that captures factual knowledge through a parallel consolidation process. In addition, the model incorporates a natural forgetting mechanism that prevents memory overloading by removing transient memory traces. Our experimental study based on a real-time first-person-shooter video game has indicated that the memory consolidation and forgetting processes are not only …
A Biologically-Inspired Affective Model Based On Cognitive Situational Appraisal, Feng Shu, Ah-Hwee Tan
A Biologically-Inspired Affective Model Based On Cognitive Situational Appraisal, Feng Shu, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Although various emotion models have been proposed based on appraisal theories, most of them focus on designing specific appraisal rules and there is no unified framework for emotional appraisal. Moreover, few existing emotion models are biologically-inspired and are inadequate in imitating emotion process of human brain. This paper proposes a bio-inspired computational model called Cognitive Regulated Affective Architecture (CRAA), inspired by the cognitive regulated emotion theory and the network theory of emotion. This architecture is proposed by taking the following positions: (1) Cognition and emotion are not separated but interacted systems; (2) The appraisal of emotion depends on and should …
Motivated Learning For The Development Of Autonomous Agents, Janusz A. Starzyk, James T. Graham, Pawel Raif, Ah-Hwee Tan
Motivated Learning For The Development Of Autonomous Agents, Janusz A. Starzyk, James T. Graham, Pawel Raif, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
A new machine learning approach known as motivated learning (ML) is presented in this work. Motivated learning drives a machine to develop abstract motivations and choose its own goals. ML also provides a self-organizing system that controls a machine’s behavior based on competition between dynamically-changing pain signals. This provides an interplay of externally driven and internally generated control signals. It is demonstrated that ML not only yields a more sophisticated learning mechanism and system of values than reinforcement learning (RL), but is also more efficient in learning complex relations and delivers better performance than RL in dynamically changing environments. In …
Preface: Trends In Natural And Machine Intelligence, Jonathan H. Chan, Ah-Hwee Tan
Preface: Trends In Natural And Machine Intelligence, Jonathan H. Chan, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Trends in natural and machine intelligence are increasingly reflecting a convergence in these two well-established fields of study. The Third International Neural Network Society Winter Conference (INNS-WC 2012) was held in Bangkok, Thailand, on October 3-5, 2012. INNS-WC2012, with an aim to bring together scientists, practitioners, and students worldwide, to discuss the past, present, and future challenges and trends in the area of natural and machine intelligence. This event has been a bi-annual conference of the International Neural Network Society (INNS) to provide a forum for international researchers to exchange latest ideas and advances on neural networks and related discipline.