Michal Chalamish

AutoMed: an automated mediator for multi-issue bilateral negotiations

In this paper, we present AutoMed, an automated mediator for multi-issue bilateral negotiation under time constraints. AutoMed elicits the negotiators preferences and analyzes them. It monitors the negotiations and proposes possible solutions for resolving the conflict. We conducted experiments in a simulated environment. The results show that negotiations mediated by AutoMed are concluded significantly faster than non-mediated ones and without any of the negotiators opting out. Furthermore, the subjects in the mediated negotiations are more satisfied with the resolutions than the subjects in the non-mediated negotiations.

Enhancing Parking Simulations Using Peer-Designed Agents

In this paper, we investigate the usefulness of peer-designed agents (PDAs) as a turn-key technology for enhancing parking simulations. The use of PDAs improves the systems ability to capture the dynamics of the interaction between individuals in the system, each theoretically exhibiting a different strategic behavior. Furthermore, since people in general are inherently rational and computation bounded, simulating this domain becomes even more challenging. The advantage of PDAs in this context lies in their ability to reliably simulate a large pool of human individuals with diverse strategies and goals. We demonstrate the efficacy of the proposed method by developing a large-scale simulation system for the parking space search domain, which plays an important role in urban transport systems. The system is based on 34 different parking search strategies. Most of these strategies are substantially different from synthetic strategies that are used in prior literature. A quantitative analysis of the PDAs indicates that they reliably capture their designers real-life strategies. Finally, we demonstrate the usefulness of PDA-based parking space search simulation by utilizing it to evaluate four different information technologies that are of increasing use in recent years.

AutoMed: an automated mediator for bilateral negotiations under time constraints

Engaging in negotiations is a daily activity. Some negotiations require the involvement of a mediator in order to be concluded in a satisfying manner. In such cases, the objective is to help the negotiators reach a mutually beneficial agreement [6, 4]. Our research focuses on mediation tools for dealing with bilateral negotiations under time constraints.

The effectiveness of peer-designed agents in agent-based simulations

The ability to reliably represent and replicate choices people make is crucial for building accurate models of day-to-day situations. The fact that people are inherently rationally-and computationally-bounded increases the difficulties in designing such simulations. This paper builds on the use of peer-designed agents PDAs --computer agents developed by people --to show their effectiveness in generating a variety of strategies and behaviors and in alleviating the simulation and behavior analysis of systems populated by human individuals with diverse strategies. The paper synthesizes the PDA-based simulation components and ideas that appear in recent PDAs literature into a cohesive simulation design, and reports a set of experiments aiming at validating the ability of PDA-based simulations to exhibit realistic behavior both in the individual agent and the system levels. The validation of individuals ability to reliably capture their strategies into PDAs is quantitative, relying on four games from different domains. The domains vary in aspects such as game complexity and the environment dynamics. The applicability of the PDA-based approach in the system level is evaluated using a large scale experiment involving 34 PDAs, each designed by a different person. All in all, the set of strategies obtained by means of PDAs is substantially richer and more varied in comparison to the limited sets of strategies used in prior multi-agent simulation studies.

Mass programmed agents for simulating human strategies in large scale systems

Simulation is an important tool for studying systems behavior under specific conditions. In recent years, agent technology has been recognized as a promising new approach for developing simulation systems, in particular, Multiagent Systems (MAS) based simulations.

Intelligent evaluation of evidence using Wigmore diagrams

In this paper, we characterize a Wigmore Diagram as an information-flow network. A fuzzy approach to weighing the strength of the evidence is defined and compared to the well-known probabilistic approach. This enables an intelligent computer evaluation of the evidence, in order to support a practicing lawyer.

Reputation Prediction of Anomaly Detection Algorithms for Reliable System

Today, sensors and/or anomaly detection algorithms (ADAs) are used to collect data in a wide variety of applications(e.g. Cyber security systems, sensor networks, etc.). Today, every sensor or ADA in its applied system participates in the collection of data throughout the entire system. The data collected from all of the sensors or ADAs are then integrated into one significant conclusion or decision, a process known as data fusion. However, the reliability, or reputation, of a single sensor or ADA may change over time, or may not be known at all. Since this reputation is taken into account when determining the final conclusion post data classification, one must be able to predict their reputations. We propose a new machine learning prediction technique (MLPT) to predict the reputation of each sensor or ADA. This technique is based on the existing Decision Tree Certainty Level technique, or DTCL, which is the creation of many random decision trees (forests) with high certainty levels [Dolev et al. (2009)]. In particular, it was shown that the DTCL enhances the classification capabilities of CARTs (Classification and Regression Trees) [Briman et al. (1984)]. After applying the DTCL technique to the reputation data, we then apply a new evolutionary process on those decision trees to reduce the overall number of trees by merging only the most accurate trees and then using only these new trees to generate the reputation values. Thus, we combine DTCL and evolution techniques to enable the determination of sensor or ADA reputations by using only the most accurate trees. Finally, we demonstrate how to improve the data fusion process by identifying the most reliable portions of the collected data to reach more accurate conclusions.

Expert-Based Fusion Algorithm of an Ensemble of Anomaly Detection Algorithms

Data fusion systems are widely used in various areas such as sensor networks, robotics, video and image processing, and intelligent system design. Data fusion is a technology that enables the process of combining information from several sources in order to form a unified picture or a decision. Today, anomaly detection algorithms (ADAs) are in use in a wide variety of applications (e.g. cyber security systems, etc.). In particular, in this research we focus on the process of integrating the output of multiple ADAs that perform within a particular domain. More specifically, we propose a two stage fusion process, which is based on the expertise of the individual ADA that is derived in the first step. The main idea of the proposed method is to identify multiple types of outliers and to find a set of expert outlier detection algorithms for each type. We propose to use semi-supervised methods. Preliminary experiments for the single-type outlier case are provided where we show that our method outperforms other benchmark methods that exist in the literature.

Semi-automatic creation of Wigmore Diagrams

The manual creation and update of a Wigmore Diagram (WD) is laborious and tedious. The system described in this paper supports the human user in building the diagram. The system transforms a list of evidence items into a WD semi-automatically by interleaving human effort and machine strength. This is done using a new data structure, a generalisation of a suffix tree.