UBICC, the Ubiquitous Computing and Communication Journal [ISSN 1992-8424], is an international scientific and educational organization dedicated to advancing the arts, sciences, and applications of information technology. With a world-wide membership, UBICC is a leading resource for computing professionals and students working in the various fields of Information Technology, and for interpreting the impact of information technology on society.
AGENT ARCHITECTURE Dr. S. Srinivasan, Mr. Amit Kumar Goel,Prof B.K Jha Head, Department Of Computer Sciences,PDM Bhadurgarh firstname.lastname@example.org Research Scholar BIT-Mesra email@example.com Department of Management,BITNoida, India firstname.lastname@example.org ABSTRACT The objective of this paper is to provide some rational, structured access to an analysis of cognitive and agent architectures.[4,5] Some familiar architectures have been considered for this preliminary analysis representing a wide range of current architectures in artificial intelligence (AI). The aim of the paper is to facilitate both an understanding of current architectures and provide insight to the development of future, improved intelligent agent architectures. The main focus is on discussing about various capabilities these architectures possess , various environments upon which these architectures act , and their memory , knowledge representation they use. Keywords: subsumption, soar, atlantis, theo, progigy, homer, icarus, ralph-mea, multiagent system 1 INTRODUCTION 2 TYPE OF AGENT ARCHITECTURE 2.1 Subsumption Architecture A complete functioning agent, whether Subsumption architecture designed by Brooks - biological, or simulated in software, or implemented 1986 .Complicated Intelligent behavior is split into in the form of a robot, needs an integrated collection Simple behavior modules Organized into layers of diverse but interrelated capabilities, i.e. .Behavior is distributed rather than centralized. architecture. At present, most work in AI and Response to stimuli is reflexive -- the perception- Cognitive Science addresses only components of such action sequence is not modulated by cognitive an architecture (e.g. vision, speech understanding, deliberation. The agents are organized in a bottom-up concept formation, rule learning, planning, motor fashion. Thus, complex behaviors are fashioned control, etc.) or mechanisms and forms of from the combination of simpler, underlying ones. No representation and inference (logic engines, explicit representation, no explicit working memory, condition-action rules, neural nets, genetic Heuristic with in each layer as long term memory , algorithms) which might be used by many No explicit problem solving mechanism ,implicit components. While such studies can make useful planning by task decomposition , no learning contributions it is important to ask, from time to time, mechanism ;Reactions to environment pre-wired into how everything can be put together, and that requires each module ,no reasoning/inference mechanism. the study of architectures. Here we briefly discuss about some familiar 2.2 Gat’s atlantis architecture architectures and their features. The study of these Integrating planning and reacting in a architectures provide a better understanding of how heterogeneous asynchronous architecture for mobile an intelligent agent should be .  agents. It consists of Control layer, sequencing layer, deliberative layer. It is hybrid reactive/deliberative robot architecture. 2.9 Soar SOAR was designed by Laird, Newell and Rosen 2.3 Theo Architecture bloom. It is the oldest and largest AI development The basic idea is Plan-Then-Compile. It means , efforts from 1983. Newell made it a developing integrating learning , planning and knowledge Architectures for intelligent agents (1990). SOAR and representation . It has a self Improving Problem BDI have a lot of commonalities. SOAR means solver . STATE OPERATOR AND RESULT. Soar use all capabilities to be handled by an intelligent agent from 2.4 ICARUS highly routine to open-ended problems. It uses Specific representation of long term memory .It uses symbols (it process symbols only ) . The underlying 3 independent asynchronous modules responsible for SOAR architecture is Symbolic System. SOAR perception , planning , effecting follows Physical symbolic System Hypothesis 2.5 Prodigy (PSSH). It says that Physical symbol system is Storing the knowledge in a form of first order necessary and sufficient condition for general predicate logic (FOPL) called Prodigy Description intelligence. The ultimate aim is to make a general language (PDL). It has a modular architecture that intelligent agent. stores the knowledge symbolically. Prodigy is an Architecture of Planning and Learning. Prodigy does It is based on production system. The production rule not subscribe to a universal learning method like Soar is to govern behavior. Production system uses but rather uses a number of different learning problem space . Problem space means set of all mechanisms. These methods allow Prodigy to be used (possible) states and a set of operators. Operators as a test-bed for exploring relationships between transforms a particular state with in problem space to problem-solving and learning . another state in the same space .It has initial stats and desired state (goal state). Operators are iteratively 2.6 Adaptive Intelligent Systems  applied to reach goal . The sequence of steps from the It can do reasoning and interact with other dynamic initial state to goal state forms the solution or entities in real time . It has problem solving behavioral path . It uses chunking (a way of learning) techniques. When encountering un-expected situation, and sub goaling. It uses appropriate knowledge it decides whether to and how to respond. It focuses (depends on the problem) such as procedural, attention on the most critical aspects of current declarative, episodic, semantic, iconic. It employs full situation. It will operate continuously without range of problem solving methods. It interacts with rebooting and also it is able to coordinate with outside world. external agent (more or less similar to human being). SOAR uses full capabilities when it tries to solve problems. Two main principle are Functionality and 2.7 Meta Reasoning Performance. It provides integral approach and this Many ideas in MAX may be traced to Prodigy. It helps to build general autonomous agents as well as rule-based forward – chaining engine that operates on specific intelligent agents for tasks such as natural productions. It is designed to support to modular language understanding , analogical reasoning , agents. They are used to respond to a dynamic planning and others. environment in a timely manner. Modules are categorized in to behavior and monitor. 2.10 Tetlon It is a problem solver. It uses two memory areas, 2.8 Homer short-Term memory and long-Term memory. Like It is not designed for general intelligence. The human beings, interruption is allowed .It has a feature underlying philosophy is to synthesize several key called Execution Cycle which always looks for what areas of AI to form one complete system, like to do next. planning, learning, natural language understanding, and robotic navigation. HOMER answers questions 2.11 Ralph-Mea posed by users and carries out instructions given by It is multiple execution architecture. Like human users. It is a modular structure. It consists of memory, being, selecting best one from the environment. planner, natural language interpreter and generator, RALPH – MEA uses Execution Architecture (EA) to reflective processes, plan executer. select from one state to best one. It uses the following: 1. Condition action Inductive 2. Action utility Learning 3. Goal – based and 4. Decision Theoretic Concept Acquisitio 2.12 Entropy Reduction Engine n Y Y Y Y It focuses on problems that require planning , Abstarctio scheduling and control . It uses many different n Y Y problem solving methods such as problem reduction , Explanati temporal projection , rule-based execution . on-based learning Y Y Y Y Y 3 CAPABILITIES. Transfer A complete functioning agent, whether biological, or of simulated in software, or implemented in the form of Learning Y a robot, needs an integrated collection of diverse but Planning Y Y Y Y Y Y Y Y Y Y Y interrelated capabilities. While such studies can make Problem useful contributions it is important to ask, from time Solving Y Y Y Y Y Y to time, how everything can be put together, and that Replannin requires the study of architectures. . The g Y Y Y Y Y Y Y capabilities are broadly split as learning, planning, Support of problem solving and reasoning. Multiple, In the following table-1A and Table-1B the rows Simultane represent the capabilities and the column corresponds -ous to agents with specific architecture. In the cells ‘Y’ Goals Y Y Y Y Y Y indicates that agent corresponds to the column Self possess the capability represented by the row. Reflection Y Y Y Y Y Y Meta- Table 1: Capabilities of Agents Reasoning Y Y Y Y Y Y Expert Meta Reasoning Architecture Adaptive Intelligence System Entropy Reduction Engine System Capability Y Y Inductive Subsumption Ralph-Mea Capability Atlantis and Prodigy Homer Tetlon Icarus Theo Soar deductive Reasoning Prediction Y Y Y Y Y Y Query Answerin g and providing Single Explanati Learning ons for Method Y Y Y Decisions Y Y Y Multi- Navigatio Method nal Learning Y Y Y Y Y Strategies Y Y Y Y Y Y Caching Y Y Natural Learning Language by Inst. Y Y Understan Learning ding Y Y by Expt. Y Y Learning Learning by by Perceptio Analogy Y Y n Y Y Y Y Y Y Y Support environment represented by the row. for Inaccurate Table 2: Environment of Agents sensing Meta Reasoning Architecture Adaptive Intelligence System Y Y Y Entropy Reduction Engine Robotic Tasks Y Y Y Y Y Y Y Subsumption Ralph-Mea Real-time Atlantis Prodigy Homer Tetlon Icarus Execution Theo Soar Focused Behavior and Processin g/ Selective Attentions Y Y Y Y Y Y Y Y Y Y Static Goal Environme Reconstru nt Y Y ction Y Dynamic Respondi Environme ng nt Y Y Y Y Y Y Y Y Y Y Intelligent Consistent ly to Environme Interrupts nt Y Y Y and Simulated Failures Y Y Y Y Y Y Y Y Environme Human- nt Y Y Y Y Y Y like Math Real Capability Y World Environme 4 ENVIRONMENT nt Y Y Y Y Y Y The world itself is a very complex place for Complex architecture agents to act in. Most architecture are Environme designed to only deal with fractions of the total nt Y Y Y Y Y Y Y possible environmental complexity by acting in Knowledg particular domains. For example, some architecture e-Rich assume that the world is static and that the only things Environme that change in the world are via an agent's actions. nt Y Y Y Other architectures may operate in dynamic Input-Rich environments but require that world be consistent or Environme predictable. nt Y An agent is anything that can be viewed as perceiving Limited its environment through sensors and acting upon that Resources Y Y Y Y environment through actuator . A Vacuum cleaner agent is a reactive agent which means it responds Complex when it senses dirt on the floor . Where as a Taxi Knowledg Driver agent should behave in a different way since e Y Y Y Y Y its environment is entirely different. It is a Goal Un- directed agent .[ 8] It has to keep all the percepts it predictable receives in its knowledge base . Y Y Y Y Y Y Y In this section various environments with which the Asynchron agents have to work are discussed. In table-2 , Rows ous Y Y indicate the environments and the column indicates Concurren the particular architecture . Y in the cell means that t the architecture corresponding to column uses the Y Y Y Varying Meta Reasoning Architecture Adaptive Intelligence System Priority Y Y Y Y Entropy Reduction Engine Memory , Knowledge & Limited Response Representation Subsumption Ralph-Mea Time Y Y Y Y Atlantis Prodigy Homer Tetlon Icarus Multiple Theo Soar Tasks Y Y Supervisor Y Y 5 MEMORY, KNOWLEDGE AND KNOWLEDGE REPRESENTATION Here the focus is to study Memory , Knowledge and Forward & Knowledge representation are handled by Agent Back-ward architecture . The aim of the paper is to understand Chaining Y Y Y the various Knowledge Representations that agents Impasse- should adapt generally . Also because of the design of driven the architecture of the agents that are taken in to Control Y Y Y Y Y consideration, the representations vary from one agent Serial to another [11,12]. Processing Y Y Y Y Y Y Parrellel A Vacuum cleaner agent is a reactive agent which Processing Y Y Y Y Y means , it responds when it senses dirt on the floor . Asynchronou Where as a Taxi Driver agent should behave in a s Processing Y Y Y different way since its environment is entirely Interruptible different. It is a Goal directed agent. [ 8 ] it has to Processing Y Y Y Y keep all the percepts it receives in its knowledge base Open-Loop . The architectures of these two typical examples processing Y Y should certainly differ greatly . Agent properties Closed - identify and entail the techniques and methods that Loop were used to realize a particular architecture or Processing Y architectural component. For example, most Hierarchical architecture includes some sort of memory. An agent Organization Y Y Y Y Y Y is said to be a knowledge-level system  when it Modular rationally brings to bear all its knowledge onto every Organization Y Y Y Y Y Y Y Y problem it attempts to solve. Thus, knowledge is the Symbolic medium of transaction at the knowledge level and the World Model Y Y Y Y Y Y Y Y Y Y behavioral law is the principle of maximum Size of the rationality. Agent properties characterize the memory: Knowledge – Is the memory declarative, procedural, and episodic? Base Y Y Y Are there size limitations? Is memory uniformly Glass Box accessed? Is it uniformly organized? These properties approach Y Y Y Y have been shown in the form a table . Black Box Approach Y Y Y Y Y In the Table 3 , Rows indicate the Memory , Declarative Knowledge and knowledge representation and the Representati column indicates the particular architecture . Y in the on Y Y Y Y Y Y cell means that the architecture corresponding to Procedural column has the type of memory or knowledge and Representati knowledge representation . on Y Y Y Y Table 3: Memory Knowledge representation Global Representati on Y Y Y Y Y Y Y Y Y Y Uniform Meta Reasoning Architecture Adaptive Intelligence System Access to Entropy Reduction Engine Knowledge Y Y Knowledge Subsumption Ralph-Mea Consistency Y Y Y Evaluation Atlantis Prodigy Homer Tetlon Icarus Homogenous Theo Soar (Uniform) Knowledge Representati on Y Y Y Y Y Hetrogenous Knowledge Representati Concept on s No-Explicit Internal Representati Architectu on Y re Y Y Associate Social Memory architectur e Y Episodic Communi Knowledge Y cation Meta- Knowledge Y Y Y Y Y Autonomy Y First-Order Pro- Logic Activity Y Representati Distributio on Y n Strips like Notation operators Usability Y representatio Expressiv n Y Y Y eness Frame - Like Refineme Representati nt on Y Y Y Dependan Network cy of representatio Models n Y Y Traceabilit y 6 EVALUATIONS Clear definition Y Based on the concepts, notation, process and Modularit pragmatics the architectures are evaluated. These are y Y Y the criteria and scales for evaluating architectures. In table-4 the column represents selected architectures Process Y and rows represent the criteria considered for Coverage of evaluation. Y stands for the architecture being workflows evaluated against particular criteria representing the Managem row . But most of the cell is blank since no proper ent methodologies are available. Also due to the lack of Complexit analytical criteria, the cost of demonstrations and y varying specifications among different architectures, Properties developing evaluation methods is a challenge [7, 10]. of process Pragmatic Table 4: Evaluations s Tool Computers and Thought, ed. Feigenbaum and Support Feldman. McGraw-Hill, New York. Connectiv  Simon, H. (1991). Cognitive Architectures in a ity rational analysis: comment. In K. VanLehn (ed.), Document Architectures for Intelligence, pp. 25-39, ation Lawrence Erlbaum Associates, Hillsdale, N.J. Usage in Projects Y  Newell, A. (1990). Unified Theories of Cognition. Harvard University Press. Cambridge, Massachusetts.  Elavine Rich , Kevin Knight and Shivshankar , CONCLUSION The problem of AI is to describe and build agents that Artificial Intelligence by The Mcgraw Hill receive percepts from the environment and perform Publishing Company limited . actions, and each such agent is implemented by a  Simon, H. (1962). The architecture of function that maps percepts to actions. It explains the complexity. Proceedings of the American role of learning as extending the reach of the designer Philosophical Society, 26, 467-482. into unknown environments, and shows how it  Artificial Intelligence: Modern Approach by constrains agent design, favoring explicit knowledge Stuart J. Russell , Peter Norvig , Prentice Hall representation and reasoning . It analyzes basic Series in Artificial Intelligence. techniques for addressing complexity . This paper has  Kurt Vanlehn , Rule-Learning Events in the tackled the question how a developer can choose acquisition of a complex skill , Journal article by, among the many development options when Journal of the Learning Science ,vol 8 , 1999 implementing an agent application. One key aspect  Philip E. Agre , Hierarchichy and History in here is to understand that agent technology currently Simon’s “Architecture of Complexity “ Journal o ers many problem speciﬁc solutions that address of Learning Science 12(3) , 2003 only certain types of application domains. We argue  Levesque H , Brachman R , A fundamental trade that one important foundation for making accurate off in Knowledge representation and reasoning , choices is the availability of well-deﬁned and 1998 comparable surveys and evaluations of artifacts such  Vincent C.Muller , Is there a future for AI as environment and capabilities. Therefore, we have without representation , springer Verlag , 2007 in a tabular form for evaluating many kinds of  A.S Maida , A Uniform architecture for rule- Architectures with respect to capability, environments based meta-reasoning and representation :- case , memory and knowledge representation . .In future study . , IEEE Computer Society Press. work we want to employ on these tables to study  Newell, A. (1982). The knowledge level. Multi-Agent System Technology. The idea is to Artificial Intelligence. 18(1), 87-127. Integrate state-of-the art AI techniques into intelligent  http://ai.eecs.umich.edu/cogarch0/ agent designs, using examples from simple, reactive agents to full knowledge-based agents with natural language capabilities and so on . This leads to the study of Multi-Agent systems and its applications. In depth analysis of various Agent architectures is to build a Multi Agent System that will be suitable for our future work on Supply Chain Management. REFERENCES  Fikes, R., Nilsson, N. (1971).strips: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2, pp. 189-203.  Firby, R. (1989). Adaptive execution in complex dynamic worlds. Ph.D. thesis. Department of Computer Science. Yale University. New Haven, Connecticut.  Newell, A. and Simon, H. (1963). GPS: A program that simulates human thought. In
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