- The main tasks of the reasoning mechanism are to realize the cooperative problem solving, and according to the current defeasible logic structure (K i , A i. - Furthermore, on the basis of the cooperative strategies, the reasoning mechanism should communicate the conclusions and the cooperative demands concerning the cooperation to the agents in other BOSs.. - The major tasks of the distributed truth maintaining mechanism are to identify the contradictions in the reasoning structural net founded according to the reasoning mechanism and to remove the conflicts by means of cooperation among multiple agents to maintain the effectiveness of the reasoning. - In this model, the key problems in cooperation are how to use effectively the experimental results of other BOSs to establish assumption, the maintenance and management of the assumptions, and how to eliminate rapidly the ill effects brought by the wrong conclusion propagations when contradictions appear.. - Only on these defeasible logic structures will the reasoning mechanism function, and just when contradictions appear in the solution, the inconsistent assumption sets are withdrawn and all their. - (2) the inconsistency may exist among the current assumption sets of various BOSs in the system, but they do not influence the effectiveness of the cooperative problem solving. - and that is because the coopera- tive process is a mutual selecting process of each other and the cooperation are implemented when no contradictions are found in the current assumption sets of both sides.. - DESIGNS AND IMPLEMENTATIONS OF ACPS WITH BOS MODEL IN THE DTIMS. - In the traveling situation assessment problem solving, there may exist uncer- tainties or mistakes in the primary input information. - In the DTIMS, the ACPS method is used to realize the cooperative problem solving and implement the mechanism mentioned above.. - In the DTIMS, all the information concerning the external environments and all the conclusions generated in interpreting and analyzing this information are repre- sented as proposition. - For example, the topographic knowledge and the features of the recogniz- able objects in the observing field are unchanged.. - The assumption proposition indicates that there is no logic basis and it is supposed to be correct by the selecting mechanism in the system according to certain rules. - problem solving. - For example, the platform assumptions, expansion assumptions, and external assumptions defined in the DTIMS may change in the process of problem solving.. - The derivation proposition means that all the conclusions are derived from other propositions according to the problem solving rules such as Expanding Rules, Fission Rules, Recognizing Rules, and so on. - The appearance of this proposition in the situation model shows mistakes in the situation analysis. - For example, that a space group is not recognized indicates there exists a mistake in the object assumption, and that a space group movement is incomplete indicates there are mistakes in the expansion assumption, and so on.. - The communication proposition is one that determines to be communicated to the agent of other BOS in accordance with the cooperative problem-solving rules.. - The design of the data structures is extremely important to the assumption- based reasoning and has a direct influence to the problem solving efficiencies. - In the DTIMS, each BOS has a Global Workspace Agent (GWA) who is a CFA and is in charge of managing shared data structures within the BOS. - According to the topographical positions, all the observing object information can be found. - Whenever a proposition is derived in the system, a new node is founded in the reasoning structural net. - S: distance between the observing position and the center position of the BOS’s sensor;. - All the derivative nodes are linked by pointers according to the deriving relations so as to form several inference tree structures, i.e., an inference structural net. - In the DTIMS, the intermediate results are classified. - So, when they are referred to by topographic positions and result types, the system can ensure that the same propositions are related on the same nodes in the inference structural net, thus giving a full play to the assumption-based inference priorities.. - Furthermore, by checking the constraint conditions, the system can find the contradictory states and then analyze the inconsistent assumption sets according to the contradiction types, the inconsistent context can be recognized and eliminated, and the assumption-based inference effectiveness is improved.. - The object assumptions and the expansion assumptions are founded respec- tively by IFA and SAA in the problem solving process. - To check if there are same conclusions in the BOS’s inference structural net.. - If there is a same conclusion in the GWA of this BOS, to increase the creditability of this conclusion. - If there is no same conclusion in the local BOS, the external conclusion is turned into an external assumption, which is then inserted in the inference structural net so that an assumption-based, problem-solving task is generated and then the procedure ends.. - Set the assumption set of the conclusion P is AS (P. - and according to the definition:. - {BOS : P} AS (P), where AS (P) is the assumption set of P in the original agent, and BOS : P denotes this external assumption from BOS.. - to refer to whether there is this node in the inference structural net. - The contradictory-identifying activities appear mainly in the problem-solving procedures of IFA, SAA, SDPA and UIA. - to determine the minimum assumption set T that can cause contradictions according to the contradiction types;. - It is of great significance to study the organizational structure of the multi-agent system for the distributed cooperative information processing, which can greatly quicken the development in many application systems. - The problem solving in BOS is neither centralized nor all localized, but distributed dynamically according to the solving tasks. - The theory behind BOS was tested and evaluated in a series of experiments in the context of the DTIMS. - The main result of the experiments was that the distributed cooperative information is processed efficiently and the hierarchical system man- agement is in perfect order, too.. - Now we are applying the BOS model to the DTIMS. - In the future, we are going to develop a software platform based the BOS model, called MBOS (Yao et al., 2001), which means multiply Basic Organization Structure for creating and deploying organizationally intelligent agents that can cooperate with other agents.. - Proceedings of the 9th National Conference on Artificial Intelligence (pp. - Communications of the ACM, 44(4).. - Proceedings of the International Conference on Econom- ics/Management and Information Technology (pp. - The State of the Art. - Assumption-based Distributed Cooperative Problem Solving.. - The presented method outlined here is motivated by and remedies a few widely recognized problems in the way customization is carried out. - It relies on a declarative specification of preconditions and effects of system’s actions and applies artificial intelligence, automated reasoning, and planning framework and techniques to dynamically recognize the lack or availability of the personal information at the precise time when it affects a system action and initiates an interaction with the user aimed at eliciting this information in case it has not yet been specified.. - Personalization has been identified as a key task to the success of many modern systems. - As Riecken writes in the editorial of the special issue of Communication of the ACM devoted to this subject (Riecken, 2000, p. - The effectiveness of a system helping a user achieve his goal, and the user’s satisfaction from interacting with the system depends critically on the user’s ability to identify and use relevant customizable options, configuring the system for optimal performance with his individual preferences and task-related information. - However, the user’s ability to provide this kind of personal informa- tion is often greatly impaired by the following drawbacks in the way personalization is implemented.. - Customization is carried out as a separate process that is taken out of context of the task in which such personal information is used, thus obscuring from the user the purpose and advantages of supplying such information.. - Further customization has to be initiated by the user.. - The items above characterize the shortcomings in the user interaction model.. - This is largely a consequence of the absence of a rigorous model of what constitutes personalization. - Theories of collaboration postulate as the key features of a collaborative activity the commitment of the parties to a shared goal, shared knowledge and communication in the effort to establish agreement and mutual knowledge of the recipe for completing the task. - Stemming directly from this view, in our approach the collaborator system has the ability to elicit personal information from the user at the time it is processing the task for which such information is critical. - This approach to personalization ensures gradual adaptation of the system to the user’s preferences.. - Recent explosion of the Internet and its ubiquity in our everyday life have created new challenges and opportunities for advancement of research on this subject, in particular, in the area of customizing information access interfaces. - Numerous works have addressed the issue of information overload and the resulting need for effective information retrieval and presentation of the results tailored to the needs of each individual visitor. - contents of a Web site or suggest a navigation path for each individual user by observing the user’s initial interaction with the Web site and matching it to the previously observed behaviors of others. - At the core of Writer’s Aid is a knowledge base that contains system’s knowledge about the state of the world, and an automated planner system. - The planner has a description of the list of actions that Writer’s Aid can execute and it can automatically combine the actions into a plan that will achieve a posted goal.. - Personalization in the Writer’s Aid consists of the initial tune-up of the system to the user’s parameters and the dynamic personalization that occurs while Writer’s Aid works on accomplishing a user-posted goal and identifies a need for informa- tion.. - The goal of the initial tune-up is to establish and enter into the system certain user-specific parameters, such as the user’s own locally stored bibliographic collections, his preferred on-line bibliogra- phies, etc.. - and in response, Writer’s Aid will generate a plan (in this case consisting of a single Action 1) described above, which accomplishes Personalization-goal-1, and thus provides Writer’s Aid with access to the user’s personal bibliographies.. - This declarative approach to the initial customization separates personalization from the rest of the code, making personalization design very flexible and more easily adjustable.. - Imagine the following scenario: Writer’s Aid is working to locate a viewable version of a paper that the user requested. - In order to avoid wasting time on searching collections of papers on subjects unrelated to the user’s research field, this action contains a precondition that the paper collection be one of the user’s preferred collections:. - Writer’s Aid does not know if ACM digital library is the user’s preferred bibliography, so it cannot establish the precondition unless it executes an action (namely Action 3 described below) of asking the user himself to obtain necessary information.. - The user’s response determines whether ACM digital library will be queried;. - Dynamic personalization occurs gradually, always within a context of a particular task, thus eliciting the user’s input at the time it is used and providing the user with knowledge of how the personal information is being used by the system.. - We have presented a novel approach to personalization that involves mixed- initiative interaction between the user and the computer system. - We are working on the implementation of semi-automatic preference gathering in Writer’s Aid and will perform laboratory user studies to investigate whether use of the proposed mechanism results in improved user satisfaction and system performance, com- pared to typical offline preference gathering.. - Personalization via knowledge preconditions remedies commonly occurring problems with customization outlined in the introduction by adopting a mixed- initiative approach to customization. - However, special attention should be given to those aspects of mixed-initiative interface that ensure the system acts in a manner that does not greatly disrupt the user’s ongoing computing activity.. - For example, an important requirement to the underlying knowledge represen- tation and planning system is non-redundancy of information gathering, as it would be annoying if the system could not infer a fact that follows from the user’s replies and it would be disastrous for the system if it ever repeated a question to the user.. - On the other hand, the user must have access to the same customization data as the system and be able (and aware of the way) to modify those settings at any time.. - A set of principles of mixed- initiative user interfaces introduced by Horvitz (1999) and the recent study of instant messaging interruption on the user’s performance in the ongoing computing activity (Cutrell, Czerwinski &. - Representing a personalization task via a set of information goals addresses the problems with the way personalization is approached in most modern systems that are outlined in the beginning of this paper in the following ways:. - It leads to preference elicitation that occurs within the context of the particular task that requires personal information, thus informing the user of his choices, motivating the response and ensuring its accuracy.. - Personalization occurs gradually at the times when the personal information is critical to the satisfaction of a user’s goal and is initiated by the computer system, thus relieving the user from potentially time-consuming task of specifying all preferences at once.. - Personalization defined declaratively via information goals separates customization of the interface from the overall system architecture making the interface more easily adjustable and extendable.. - Experience with personalization of Yahoo! Communications of the ACM . - Proceedings of the Third International Conference on Autonomous Agents (Agents’99) (pp. - Communications of the ACM . - The concept of personalization has long been advocated to be one of the edges to improve the stickiness of on-line stores. - Limitations and future extensions of the proposed system are also discussed.. - Within the past few years, a large variety of on-line stores have been started in the cyberspace. - Along this direction, the concept of personalization has long been advocated as one of the edges to improve the stickiness of on-line stores. - Roy, 1999) where user models are created implicitly by applying machine learning or information retrieval techniques to analyze user preference ratings and corresponding product features (e.g., the products that a customer rated high so far have the common attributes of being less colorful, easy to clean, and safe).. - In the literature, there exist a lot of works on content-based and collaborative recommender systems. - Sen, Dutta, and Mukherjee (2000) proposed an intelligent buyer agent which aims to educate the user to be a more informed customer by understanding the user query and providing alternatives using a pre-built domain-specific knowledge base, which is based on propositional logic representation. - “how can the user information requirement be minimized while an acceptable level of recommendation service can still be provided?” In this paper, we restrict the user information needed to only demographic information and describe in details how a related knowledge-based system can be built to support an adaptive on-line store in providing customized recommendation services. - The limitations and future extensions of the proposed framework will also be discussed.. - A typical example is the rule-based system where the knowledge base is represented in the form of a set of if-then rules and forward-chaining reasoning is used in the inference engine
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