• The Journal of the Korea Contents Association
• /
• v.14 no.9
• /
• pp.1-13
• /
• 2014

Apart from the physical realism, the implementation of various physical actions of an agent to respond to dynamically changing situations is essential for the design of an agent in a cyber world. To achieve a maximum diversity in actions, we develop a mechanism that allows composite actions to be constructed by reusing a set of primitive motions and enables an agent to instantly react to changes in the ambient states. Specifically we model an agent's body in terms of joints, and a primitive or composite motion is performed in a real time. To implement this mechanism, we produce an animation for basic joint movements and develop a method to construct overall motions out of the primitive motions. These motions can be assembled into a plan by which an agent can achieve a goal. In this manner, diverse actions can be implemented without excessive efforts. This approach has conspicuous advantages when constructing a parallel action, e.g., eating while walking, that is, two or more parallel actions can be naturally merged into a parallel action according to their priority. We implement several composite and parallel actions to demonstrate the viability of our approach.

• International Journal of Control, Automation, and Systems
• /
• v.6 no.6
• /
• pp.904-914
• /
• 2008

An action-selection-mechanism(ASM) has been proposed to work as a fully connected finite state machine to deal with sequential behaviors as well as to allow a state in the task program to migrate to any state in the task, in which a primitive node in association with a state and its transitional conditions can be easily inserted/deleted. Also, such a primitive node can be learned by a shortest path-finding-based reinforcement learning technique. Specifically, we define a behavioral motivation as having state-dependent value as a primitive node for action selection, and then sequentially construct a network of behavioral motivations in such a way that the value of a parent node is allowed to flow into a child node by a releasing mechanism. A vertical path in a network represents a behavioral sequence. Here, such a tree for our proposed ASM can be newly generated and/or updated whenever a new behavior sequence is learned. To show the validity of our proposed ASM, experimental results of a mobile robot performing the task of pushing- a- box-in to- a-goal(PBIG) will be illustrated.

• IEIE Transactions on Smart Processing and Computing
• /
• v.1 no.3
• /
• pp.192-200
• /
• 2012

In order for humans and robots to interact in daily life, robots need to understand human speech and link it to their actions. This paper proposes a hierarchical behavior model for robot action control using natural language commands. The model, which consists of episodes, primitive actions and atomic functions, uses a sentential cognitive system that includes multiple modules for perception, action, reasoning and memory. Human speech commands are translated to sentences with a natural language processor that are syntactically parsed. A semantic parsing procedure was applied to human speech by analyzing the verbs and phrases of the sentences and linking them to the cognitive information. The cognitive system performed according to the hierarchical behavior model, which consists of episodes, primitive actions and atomic functions, which are implemented in the system. In the experiments, a possible episode, "Water the pot," was tested and its feasibility was evaluated.

• The Journal of the Korea Contents Association
• /
• v.11 no.7
• /
• pp.9-20
• /
• 2011

In this paper, we attempt to realize the diversity of human actions in a virtual world in terms of the basic actions. We utilize the comprehensive knowledge structure of ontology to organize the human actions into an action hierarchy, each action being the root action of its associated hierarchy of specialized actions. Each basic action is implemented by composing the primitive motions and each basic action in turn by reusing those basic actions. Our approach to the development of these actions is focused more on their diversity and brevity than on their visual realism. Based on this design we develop a method to grasp virtual humans' characteristics and reflect those characteristics on their respective action patterns. We also present a mechanism for the agents to adapt their motions to diverse qualitative changes in environment. We apply these methods to such basic actions as walk, run and throw to demonstrate their viability.

• International Journal of Contents
• /
• v.15 no.4
• /
• pp.16-26
• /
• 2019

The efficient implementation of various physical actions of agents to respond to dynamically changing situations is essential for the simulation of realistic agents and activities in a cyber world. To achieve a maximum diversity of actions and immediate responsiveness to abrupt changes in situations, we have developed an animation technique in which complex actions are recursively constructed by reusing a set of primitive motions, and agents are designed to react in real-time to abrupt ambient changes by computationally satisfying kinematic constraints on body parts with respect to their goals. Our reusing scheme is extended to visualize the procedure of realistic intricate situations involving many concurring events. Our approach based on motion reuse and recursive assembly has clear advantages in motion variability and action diversity with respect to authoring scalability and motion responsiveness compared to conventional monolithic (static) animation techniques. This diversity also serves to accommodate the characteristic unpredictability of events concurring in a situation due to inherent non-determinism of associated conditions. To demonstrate the viability of our approach, we implement several composite and parallel actions in a dynamically changing example situation involving events that were originally independent until coincidentally inter-coupled therein.

• East Asian mathematical journal
• /
• v.35 no.3
• /
• pp.357-363
• /
• 2019

Let the circle act on a compact almost complex manifold M with a non-empty discrete fixed point set. To each fixed point, there are associated non-zero integers called weights. A positive weight w is called primitive if it cannot be written as the sum of positive weights, other than w itself. In this paper, we show that if every weight is primitive, then the Todd genus Todd(M) of M is positive and there are $Todd(M){\cdot}2^n$ fixed points, where dim M = 2n. This generalizes the result for symplectic semi-free actions by Tolman and Weitsman [8], the result for semi-free actions on almost complex manifolds by the author [6], and the result for certain symplectic actions by Godinho [1].

• Bulletin of the Korean Mathematical Society
• /
• v.60 no.4
• /
• pp.1025-1034
• /
• 2023

Let U be the restricted quantized enveloping algebra Ũ_q(𝖘𝖑₂) over an algebraically closed field of characteristic zero, where q is a primitive 𝑙-th root of unity (with 𝑙 being odd and greater than 1). In this paper we show that any indecomposable submodule of U under the adjoint action is generated by finitely many special elements. Using this result we describe all ideals of U. Moreover, we classify annihilator ideals of simple modules of U by generators.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.2027-2032
• /
• 2003

For action selection as well as learning, simple associations between stimulus and response have been employed in most of literatures. But, for a successful task accomplishment, it is required that an animat can learn and express behavioral sequences. In this paper, we propose a novel action-selection-mechanism to deal with sequential behaviors. For this, we define behavioral motivation as a primitive node for action selection, and then hierarchically construct a network with behavioral motivations. The vertical path of the network represents behavioral sequences. Here, such a tree for our proposed ASM can be newly generated and/or updated, whenever a new sequential behaviors is learned. To show the validity of our proposed ASM, three 2-D grid world simulations will be illustrated.

• Proceedings of the KIEE Conference
• /
• 2004.11c
• /
• pp.496-498
• /
• 2004

An action-selection-mechanism is proposed to deal with sequential behaviors, where associations between some of stimulus and behaviors will be learned by a shortest-path-finding-based reinforcement team ins technique. To be specific, we define behavioral motivation as a primitive node for action selection, and then sequentially construct a network with behavioral motivations. The vertical path of the network represents a behavioral sequence. Here, such a tree fur our proposed ASM can be newly generated and/or updated. whenever a new sequential behaviors is learned. To show the validity of our proposed ASM, some experimental results on a "pushing-box-into-a-goal task" of a mobile robot will be illustrated.

• Proceedings of the KIEE Conference
• /
• 2002.11c
• /
• pp.178-182
• /
• 2002

For action selection as well as teaming, simple associations between stimulus and response have been employed in most of literatures. But, for successful task accomplishment, it is required that artificial life system can team and express behavioral sequences. In this paper, we propose a novel action-selection-mechanism to deal with behavioral sequences. For this, we define behavioral motivation as a primitive node for action selection, and then hierarchically construct a tree with behavioral motivations. The vertical path of the tree represents behavioral sequences. Here, such a tree for our proposed ASM can be newly generated and/or updated, whenever a new behavioral sequence is learned. To show the validity of our proposed ASM, three 2-D grid world simulations will be illustrated.