47142619
Semantic Resolution for E-commerce
We describe a research project on resolving semantic differences for multi-agent systems (MAS) in electronic commerce. The approach can be characterized as follows: (1) agents in a MAS may have their own specific ontologies defined on top of a shared base ontology; (2) concepts in these ontologies are represented as frame-like structures based on DAML+OIL language; (3) the semantic differences between agents are resolved at runtime through inter-agent communication; and (4) the resolution is viewed as an abductive inference process, and thus necessarily involves approximate reasoning.
9845111
Quantitative Agent Service Matching
The ultimate goal of service matching is to find the service provider(s) that would perform tasks of given description with the best overall degree of satisfaction. However, service description matching solves only part of the problem. Agents that match a given request may vary greatly in their actual capabilities to perform the tasks, and an agent may have strong and weak areas. In this work, we take a quantitative approach in which performance rating is considered an integral part of an agent's capability model and service distribution is taken into account in determining the degree of match. With the dynamic refinement of the agent capability model, the broker captures an agent's performance levels as well as its strong and weak areas. An experimental system has been designed and implemented within the OWL/OWL-S framework and the results statistics show significant advantage over other major levels of brokers.
1517191
Semantic resolution for e-commerce
We describe a research project on resolving semantic differences for multi-agent systems (MAS) in electronic commerce. The approach can be characterized as follows: (1) agents in a MAS may have their own specific ontologies defined on top of a shared base ontology; (2) concepts in these ontologies are represented as frame-like structures based on DAML+OIL language; (3) the semantic differences between agents are resolved at runtime through inter-agent communication; and (4) the resolution is viewed as an abductive inference process, and thus necessarily involves approximate reasoning.
183316
Modifying Bayesian Networks by Probability Constraints
This paper deals with the following problem: modify a Bayesian network to satisfy a given set of probability constraints by only change its conditional probability tables, and the probability distribution of the resulting network should be as close as possible to that of the original network. We propose to solve this problem by extending IPFP (iterative proportional fitting procedure) to probability distributions represented by Bayesian networks. The resulting algorithm E-IPFP is further developed to D-IPFP, which reduces the computational cost by decomposing a global E-IPFP into a set of smaller local E-IPFP problems. Limited analysis is provided, including the convergence proofs of the two algorithms. Computer experiments were conducted to validate the algorithms. The results are consistent with the theoretical analysis.
723568
A Bayesian Network Approach to Ontology Mapping
This paper presents our ongoing effort on developing a principled methodology for automatic ontology mapping based on BayesOWL, a probabilistic framework we developed for modeling uncertainty in semantic web. In this approach, the source and target ontologies are first translated into Bayesian networks (BN); the concept mapping between the two ontologies are treated as evidential reasoning between the two translated BNs. Probabilities needed for constructing conditional probability tables (CPT) during translation and for measuring semantic similarity during mapping are learned using text classification techniques where each concept in an ontology is associated with a set of semantically relevant text documents, which are obtained by ontology guided web mining. The basic ideas of this approach are validated by positive results from computer experiments on two small real-world ontologies.
7138939
A probabilistic extension to ontology language OWL
To support uncertain ontology representation and ontology reasoning and mapping, we propose to incorporate Bayesian networks (BN), a widely used graphic model for knowledge representation under uncertainty and OWL, the de facto industry standard ontology language recommended by W3C. First, OWL is augmented to allow additional probabilistic markups, so probabilities can be attached with individual concepts and properties in an OWL ontology. Secondly, a set of translation rules is defined to convert this probabilistically annotated OWL ontology into the directed acyclic graph (DAG) of a BN. Finally, the BN is completed by constructing conditional probability tables (CPT) for each node in the DAG. Our probabilistic extension to OWL is consistent with OWL semantics, and the translated BN is associated with a joint probability distribution over the application domain. General Bayesian network inference procedures (e.g., belief propagation or junction tree) can be used to compute P(C/spl bsol/e): the degree of the overlap or inclusion between a concept C and a concept represented by a description e. We also provide a similarity measure that can be used to find the most similar concept that a given description belongs to.
42292049
Fitting semiparametric cure models
Survival data with a sizable cure fraction are commonly encountered in some cancer clinical researches and the semiparametric proportional hazards cure model has been recently investigated to analyze such data. However the estimation method of the model requires a special C program. The restrictive assumptions of the model also limit its application in broader settings. We present a new computational method for the cure model in this article. The method combines the computational methods for logistic regression and the Cox proportional hazards models and is easy to implement in many statistical packages. We also show how this method allows a number of useful extensions of the model to relax the restrictive assumptions. An illustrative example with survival times of lymphoma patients is provided.
15008277
The interoperability problem: bringing together mobile agents and agent communication languages
Interoperability is a central issue far both the mobile agents community and the wider agents community. Unfortunately, the interoperability concerns are different between the two communities. As a result, inter-agent communication is an issue that has been addressed in a limited manner by the mobile agents community. Supplementing mobile agents with the ability to interact a a higher level with other mobile or static agents, or agentified information sources is a necessity in the vastly heterogeneous arena where mobile agents are called to compete. An agent communication language (ACL) has been interpreted by the agents community as a tool with the capacity to integrate disparate sources of information. Such a perspective does not seem to be shared by the mobile agents community. We investigate the origins of the differences of perspective on agent to agent communication and examine the reasons for the relative lack of interest on ACLs by mobile agents researchers and explore the integration of ACLs into mobile agents frameworks.
16284083
Minimizing Complexity in Cellular Automata Models of Self-Replication
Understanding self-replication from an information processing perspective is important because, among other things, it can shed light on molecular mechanisms of biological reproduction and on prebiotic chemical evolution. Intuition, biological knowledge, and early computational models of self-replication all suggested that self-replication is an inherently complex process. In this paper we describe recent computational studies that challenge this viewpoint. We summarize our recent work with cellular automata models of simple yet non-trivial self-replicating structures called unsheathed loops. For example, one unsheathed loop consists of only six components and requires only 20 rules to specify the local intercomponent interactions needed to bring about replication. The implication of this work is that, when viewed as an emergent property of numerous local, concurrent interactions between components, self-replicating systems can be substantially simpler than is generally recognized.
5372255
Holes in Graphs
The celebrated Regularity Lemma of Szemeredi asserts that every sufficiently large graph $G$ can be partitioned in such a way that most pairs of the partition sets span $\epsilon$-regular subgraphs. In applications, however, the graph $G$ has to be dense and the partition sets are typically very small. If only one $\epsilon$-regular pair is needed, a much bigger one can be found, even if the original graph is sparse. In this paper we show that every graph with density $d$ contains a large, relatively dense $\epsilon$-regular pair. We mainly focus on a related concept of an $(\epsilon,\sigma)$-dense pair, for which our bound is, up to a constant, best possible.
18522080
Example of supersonic solutions to a steady state Euler-Poisson system
Abstract We give an example of supersonic solutions to a one-dimensional steady state Euler–Poisson system arising in the modeling of plasmas and semiconductors. The existence of the supersonic solutions which correspond to large current density is proved by Schauder’s fixed point theorem. We show also the uniqueness of solutions in the supersonic region.