A study on environmental adaptation and expansion of intelligent agent 


Vol. 10,  No. 7, pp. 795-802, Dec.  2003
10.3745/KIPSTB.2003.10.7.795


PDF
  Abstract

To live autonomously, intelligent agents such as robots or virtual characters need ability that recognizes given environment, and learns and chooses adaptive actions. So, we propose an action selection/learning mechanism in intelligent agents. The proposed mechanism employs a hybrid system which integrates a behavior-based method using the reinforcement learning and a cognitive-based method using the symbolic learning. The characteristics of our mechanism are as follows. First, because it learns adaptive actions about environment using reinforcement learning, our agents have flexibility about environmental changes. Second, because it learns environmental factors for the agent´s goals using inductive machine learning and association rules, the agent learns and selects appropriate actions faster in given surrounding and more efficiently in extended surroundings. Third, in implementing the intelligent agents, we considers only the recognized states which are found by a state detector rather than by all states. Because this method consider only necessary states, we can reduce the space of memory. And because it represents and processes new states dynamically, we can cope with the change of environment spontaneously.

  Statistics
  Cite this article

[IEEE Style]

B. H. Jeong and P. Y. Taeg, "A study on environmental adaptation and expansion of intelligent agent," The KIPS Transactions:PartB , vol. 10, no. 7, pp. 795-802, 2003. DOI: 10.3745/KIPSTB.2003.10.7.795.

[ACM Style]

Baeg Hye Jeong and Park Yeong Taeg. 2003. A study on environmental adaptation and expansion of intelligent agent. The KIPS Transactions:PartB , 10, 7, (2003), 795-802. DOI: 10.3745/KIPSTB.2003.10.7.795.

Forms

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)

Old Journals

Indexing

All publications of TKIPS is indexed in DOI, EBSCO, Google Scholar, Crossref, and CrossCheck.

TKIPS is also selected as the Journal for Accreditation by NRF (National Research Foundation of Korea).

Related Journals