2560128
Towards Human Decision-Making in Multi-Agent Systems
2 Decision theory finds its roots as a formal topic of research in the fields of economics and psychology. It traditionally relied upon the formal description of decision making embodied principle of maximum expected utility (EUT). But EUT can not capture the common human decision-making attitudes because it does not model the domain of human intuitions – thoughts or preferences that come to mind quickly and without much reflection. This paper models one human’s intuitive attitudes toward risk: risk-aversive and risk-seeking. Rather than immediately making a choice directly from the set of alternatives, agents first form an attitude concerning each possible alternative and likewise solve the corresponding decision problem with a preference order. Correspondingly, this preference order should not be constructed solely on the basis of the individual agent’s own attitudes but with the attitudes of all agents which are relevant. This paper presents our work on above two issues: capturing human intuitive decision-making under risk and extending it to social settings. Finally, we present two experiments that explore risk management behaviors of agents in social settings.
14748203
A history sensitive cascade model in diffusion networks
Diffusion is a process by which information, viruses, ideas and new behavior spread over social networks. The traditional independent cascade model gives activated nodes a one-time chance to activate each of its neighboring nodes with some probability. This paper extends the traditional cascade model to be history dependent. We propose a new model called the History Sensitive Cascade Model (HSCM) that allows activated nodes to receive more than a one-time chance to activate their neighbors. HSCM provides 1) a polynomial algorithm for calculating the probability of activity for any arbitrary node at any arbitrary time in tree structure graphs, and 2) a Markov model for calculating the probability in general graphs. Finally, we perform an empirical study on HSCM under different network settings. These simulations have showed its power to observe and explain the emergent phenomena in the macro level when changing parameters in the micro level.
2679883
Observation-based proactive communication in team cooperation
Observation and communication are important aspects of agent teamwork. We employ the agent's observability as the major means for individual agents to reason about the environment and other team members. We focus on how to represent agents' observability and how to include it into the basic reasoning for proactive communication. The syntax and the operational semantics of observability are given. Preliminary experiments are carried out to study the effectiveness of different aspects of observability.
16356479
A multi-agent simulation for social agents
Agent-based modeling of human social behavior is an increasingly important research area. Many fully functional multi-agent systems have been developed and have been put to use over the years. But efficient scalable and robust social systems are difficult to engineer, both from the modeling perspective and the implementation perspective. There exist three important difficulties. First, the system needs to have an adaptable agent framework that can successfully make intuitive and deliberative decisions much like a real social participant would. Secondly, the system must have a robust architecture that not only ensures its functioning no matter the simulation, but also provides an easily understood interface that researchers can interact with while running their simulations. Finally, the system must be effectively distributed to handle the necessary number of agents that social research requires to obtain meaningful results. This paper presents our work on creating a multi-agent simulation for social agents that would overcome these three difficulties. Lastly, we present a simulation to test the system and provide the results of that simulation.
5679790
Proactive Information Exchange During Team Cooperation
We are concerned, ultimately, with multi-agent teams involving both software and human agents in which the software agents assist in training the humans, e.g., as virtual team members. The software agents must be capable of utilizing the teamwork mechanisms used by humans. In this paper, we describe how to give agents the same implicit communication capabilities, i.e., observation, that humans use. We show how agents can use observations of the environment and teammates' actions to estimate other agents beliefs without bothering them with unnecessary messages; we also show how agents can anticipate information needs among the team and proactively exchange information, reducing the total communication volume. To achieve these goals, we add sensing capabilities to agents and present algorithms that infer teammates' mental state from the observation. Experimental data is presented that shows that the advantage of observation in proactive information exchange as well as enhanced team performance.
1229519
A decision-theoretic approach for designing proactive communication in multi-agent teamwork
Techniques that support effective communication during teamwork processes are of particular importance. Psychological study shows that an effective team often can anticipate information exchange among the team and communicate relevant information proactively. Proactive communication is crucial for understanding and sharing common goals and for cooperative actions. Communication can be valuable if it assists agents with new and timely information; it also has cost because it consumes network resources such as bandwidth. To address these issues, we present a new model that uses information production and need to capture the complex multi-agent communication process and a dynamic decision-theoretic determination of communication strategies. We also introduce a generic utility function and an algorithm, DTPC (Decision-Theoretic Proactive Communication), that focuses on representing information production and need of team members and resolving decision interactions among them for making decisions.
27426921
Cryptographic logical relations
Using contextual equivalence (a.k.a. observational equivalence) to specify security properties is an important idea in the field of formal verification of cryptographic protocols. While contextual equivalence is difficult to prove directly, one is usually able to deduce it using the so-called logical relations in typed @l-calculi. We apply this technique to the cryptographic metalanguage-an extension of Moggi's computational @l-calculus, where we use Stark's model for name creation to explore the difficult aspect of dynamic key generation. The categorical construction of logical relations for monadic types (by Goubault-Larrecq et al.) then allows us to derive logical relations over the category Set^I. Although Set^I is a perfectly adequate model of dynamic key generation, it lacks in some aspects when we study relations between programs in the metalanguage. This leads us to an interesting exploration of what should be the proper category to consider. We show that, to define logical relations in the cryptographic metalanguage, a better choice of category is Set^I^^^-> that we proposed in [Y. Zhang, D. Nowak, Logical relations for dynamic name creation, in: Proceedings of the 17th International Workshop of Computer Science Logic and the 8th Kurt Godel Colloqium, CSL & KGL, in: Lecture Notes in Computer Science, vol. 2803, Springer-Verlag, 2003, pp. 575-588]. However, this category is still lacking in some subtler aspects and we propose a refined category Set^P^I^^^-> to fix the flaws, but our final choice is Set^I^x^I, which is equivalent to Set^P^I^^^->. We define the contextual equivalence based on Set^I^x^I and show that the cryptographic logical relation derived over Set^I^x^I is sound and can be used to verify protocols in practice.
10735385
Data-Driven Feature-Based 3D Face Synthesis
This paper presents a novel data-driven method for creating varied realistic face models by synthesizing a set of facial features according to intuitive high-level control parameters. Our method takes as examples 3D face scans in order to exploit the variations presented in the real faces of individuals. We use an automatic model fitting approach for the 3D registration problem. Once we have a common surface representation for each example, we form feature shape spaces by applying principal component analysis (PCA) to the data sets of facial feature shapes. Using PCA coefficients as a compact shape representation, we approach the shape synthesis problem by forming scattered data interpolation functions that are devoted to the generation of desired shape by taking the anthropometric parameters as input. The correspondence among all exemplar textures is obtained by parameterizing a 3D generic mesh over a 2D image domain. The new feature texture with desired attributes is synthesized by interpolating the example textures. Apart from an initial tuning of feature point positions and assignment of texture attribute values, our method is fully automated.
11500894
Using Reactive Deliberation for Real-Time Control of Soccer-Playing Robots
Soccer meets the requirements of the Situated Agent approach and as a task domain is sufficiently rich to support research integrating many branches of Al. Reactive deliberation is a robot architecture that combines responsiveness to the environment with intelligent decision making. Under Reactive Deliberation, the robot controller is partitioned into a deliberator and an executor; the distinction is primarily based on the different time scales of interaction. A controller for our team entry in the Robocup97 Simulation League, UBC Dynamo97, has been developed using the Reactive Deliberation architecture.
4002740
Selectivity Estimation by Batch-Query based Histogram and Parametric Method
Histograms are used extensively for selectivity estimation and approximate query processing. Workload-aware dynamic histograms can self-tune itself based on query feedback without scanning or sampling the underlaying datasets in a systematic and comprehensive way. Dynamic histograms allocate more buckets not only for the areas with most skewed data distribution but also according to users' interest. However, it takes long time to 'warm-up' (i.e., a large number of queries need to be processed before the histogram can provide a satisfactory coverage and accuracy). Thus, it is less effective to adapt with workload pattern changes. In this paper, we propose a novel online query scheduling algorithm which can significantly reduce the warm-up time for dynamic histograms. A parametric method is proposed to remedy the problem of inaccurate query selectivity estimation for the areas with poor histogram coverage. Experimental results demonstrate a significant effectiveness and accuracy improvement of our approach.
38299104
A Computational Trust Model for Semantic Web Based on Bayesian Decision Theory
Enabling trust to ensure more effective and efficient agent interaction is at the heart of the Semantic Web vision. We propose a computational trust model based on Bayesian decision theory in this paper. Our trust model combines a variety of sources of information to assist users with making correct decision in choosing the appropriate providers according to their preferences that expressed by prior information and utility function, and takes three types of costs (operational, opportunity and service charges) into account during trust evaluating. Our approach gives trust a strict probabilistic interpretation and lays solid foundation for trust evaluating on the Semantic Web.