• Journal of the Korean Operations Research and Management Science Society
• /
• v.20 no.2
• /
• pp.159-178
• /
• 1995

Supervisory control is an approach based on formal language. it is used to model and control discrete event systems in which each discrete event process is represented as an automation. A supervisor is a generator that switches control patterns in such a way that a given discrete evenet process behaves in obedience to various constraints. A flexible manufacturing cell (FMC) is one of discrete evenet systems. Functions necessary for the operation of an FMC are characterized by operational components and informational compoments. The operational components can be modeled using the finite state machines and the informational components can be modeled using the abstract formalism which describes supporting operations of the cell controller. In this paper, we addressed function required for FMC control specification, software engineering aspects on FMC control based on supervisory control, a concept of event queue for resolving synchronization problem, and complexity reduction. Based on the mathematical model of an FMC. we synthesized the controller by integrating a supervisor for FMC with control specification that specifies event-driven operation of the cell controller. The proposed control scheme is stable mathematically so that the system always behaves on a controlled way even under the existence of uncontrollable events. Furthermore, using an event queue concept, we can solve a synchronization problem caused by the violation of instantaneity assumption of supervisory control theory in real life situation. And also, we can propotype a control software rapidly due to the modularity of the proposed control scheme.

• Journal of KIISE:Information Networking
• /
• v.28 no.3
• /
• pp.377-388
• /
• 2001

An approach for testing multi-protocol Implementation Under Test (IUT) with a single test suite has been proposed in[1]. this paper proposes an algorithm called Multi-protocol Test Method (MPTM) for automatic test case generation based on that approach. With the MPTM, a multi-protocol IUT consisting of two protocol layers is modeled as two Finite State Machines (FSMs), and the relationships between the transitions of the two FSMs are defined as a set of transition relationships pre-execution and carried-by. The proposed algorithm is implemented and applied to a simplified TCP/IP and B-ISDN Signaling/SSCOP. MPTM is able to test the multi-protocol IUT even though the interfaces between the protocol layers are not exposed. It results in that the proposed MPTM allows the same test coverage as conventional test methods even with fewer numbers of test cases.

• Journal of KIISE:Software and Applications
• /
• v.35 no.3
• /
• pp.179-188
• /
• 2008

An important issue in multiagent reinforcement learning is how an agent should team its optimal policy through trial-and-error interactions in a dynamic environment where there exist other agents able to influence its own performance. Most previous works for multiagent reinforcement teaming tend to apply single-agent reinforcement learning techniques without any extensions or are based upon some unrealistic assumptions even though they build and use explicit models of other agents. In this paper, basic concepts that constitute the common foundation of multiagent reinforcement learning techniques are first formulated, and then, based on these concepts, previous works are compared in terms of characteristics and limitations. After that, a policy model of the opponent agent and a new multiagent reinforcement learning method using this model are introduced. Unlike previous works, the proposed multiagent reinforcement learning method utilize a policy model instead of the Q function model of the opponent agent. Moreover, this learning method can improve learning efficiency by using a simpler one than other richer but time-consuming policy models such as Finite State Machines(FSM) and Markov chains. In this paper. the Cat and Mouse game is introduced as an adversarial multiagent environment. And effectiveness of the proposed multiagent reinforcement learning method is analyzed through experiments using this game as testbed.

• Journal of Multimedia Information System
• /
• v.5 no.3
• /
• pp.147-154
• /
• 2018

Natural language processing (NLP) is an emerging research area in which we study how machines can be used to perceive and alter the text written in natural languages. We can perform different tasks on natural languages by analyzing them through various annotational tasks like parsing, chunking, part-of-speech tagging and lexical analysis etc. These annotational tasks depend on morphological structure of a particular natural language. The focus of this work is part-of-speech tagging (POS tagging) on Hindi language. Part-of-speech tagging also known as grammatical tagging is a process of assigning different grammatical categories to each word of a given text. These grammatical categories can be noun, verb, time, date, number etc. Hindi is the most widely used and official language of India. It is also among the top five most spoken languages of the world. For English and other languages, a diverse range of POS taggers are available, but these POS taggers can not be applied on the Hindi language as Hindi is one of the most morphologically rich language. Furthermore there is a significant difference between the morphological structures of these languages. Thus in this work, a POS tagger system is presented for the Hindi language. For Hindi POS tagging a hybrid approach is presented in this paper which combines "Probability-based and Rule-based" approaches. For known word tagging a Unigram model of probability class is used, whereas for tagging unknown words various lexical and contextual features are used. Various finite state machine automata are constructed for demonstrating different rules and then regular expressions are used to implement these rules. A tagset is also prepared for this task, which contains 29 standard part-of-speech tags. The tagset also includes two unique tags, i.e., date tag and time tag. These date and time tags support all possible formats. Regular expressions are used to implement all pattern based tags like time, date, number and special symbols. The aim of the presented approach is to increase the correctness of an automatic Hindi POS tagging while bounding the requirement of a large human-made corpus. This hybrid approach uses a probability-based model to increase automatic tagging and a rule-based model to bound the requirement of an already trained corpus. This approach is based on very small labeled training set (around 9,000 words) and yields 96.54% of best precision and 95.08% of average precision. The approach also yields best accuracy of 91.39% and an average accuracy of 88.15%.