Search engines are perceived as a reliable source for general information needs. However, finding the answer to medical questions using search engines can be challenging for an ordinary user. Content can be biased and results may present different opinions. In addition, interpreting medically related content can be difficult for users with no medical background. All of these can lead users to incorrect conclusions regarding health related questions.

The design of automated negotiators has been the focus of abundant research in recent years. However, due to difficulties involved in creating generalized agents that can negotiate in several domains and against human counterparts, many automated negotiators are domain specific and their behavior cannot be generalized for other domains. Some of these difficulties arise from the differences inherent within the domains, the need to understand and learn negotiators’ diverse preferences concerning issues of the domain, and the different strategies negotiators can undertake. In this paper we present a system that enables alleviation of the difficulties in the design process of general automated negotiators termed Genius, a General Environment for Negotiation with Intelligent multi‐purpose Usage Simulation. With the constant introduction of new domains, e‐commerce and other applications, which require automated negotiations, generic automated negotiators encompass many benefits and advantages over agents that are designed for a specific domain. Based on experiments conducted with automated agents designed by human subjects using Genius we provide both quantitative and qualitative results to illustrate its efficacy. Finally, we also analyze a recent automated bilateral negotiators competition that was based on Genius. Our results show the advantages and underlying benefits of using Genius and how it can facilitate the design of general automated negotiators.

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 system's 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.

Coordination in mixed agent-human environments is an important, yet not a simple, problem. Little attention has been given to the issues raised in teams that consist of both computerized agents and people. In such situations different considerations are in order, as people tend to make mistakes and they are affected by cognitive, social and cultural factors. In this paper we present a novel agent designed to proficiently coordinate with a human counterpart. The agent uses a neural network model that is based on a pre-existing knowledge base which allows it to achieve an efficient modeling of a human's decisions and predict their behavior. A novel communication mechanism which takes into account the expected effect of communication on the other member will allow communication costs to be minimized. In extensive simulations involving more than 200 people we investigated our approach and showed that our agent achieves better coordination when involved, compared to settings in which only humans or another state-of-the-art agent are involved.

The interaction of people with autonomous agents has become increasingly prevalent. Some of these settings include security domains, where people can be characterized as uncooperative, hostile, manipulative, and tending to take advantage of the situation for their own needs. This makes it challenging to design proficient agents to interact with people in such environments. Evaluating the success of the agents automatically before evaluating them with people or deploying them could alleviate this challenge and result in better designed agents. In this paper we show how Peer Designed Agents (PDAs) -- computer agents developed by human subjects -- can be used as a method for evaluating autonomous agents in security domains. Such evaluation can reduce the effort and costs involved in evaluating autonomous agents interacting with people to validate their efficacy. Our experiments included more than 70 human subjects and 40 PDAs developed by students. The study provides empirical support that PDAs can be used to compare the proficiency of autonomous agents when matched with people in security domains.

Computer agents are increasingly deployed in settings in which they make decisions with people, such as electronic commerce, collaborative interfaces, and cognitive assistants. However, the scientific evaluation of computational strategies for human-computer decision-making is a costly process, involving time, effort and personnel. This paper investigates the use of Peer Designed Agents (PDA) — computer agents developed by human subjects — as a tool for facilitating the evaluation process of automatic negotiators that were developed by researchers. It compared the performance between automatic negotiators that interacted with PDAs to automatic negotiators that interacted with actual people in different domains. The experiments included more than 300 human subjects and 50 PDAs developed by students. Results showed that the automatic negotiators outperformed PDAs in the same situations in which they outperformed people, and that on average, they exhibited the same measure of generosity towards their negotiation partners. These patterns were significant for all types of domains, and for all types of automated negotiators, despite the fact that there were individual differences between the behavior of PDAs and people. The study thus provides an empirical proof that PDAs can alleviate the evaluation process of automatic negotiators, and facilitate their design.

The use of unmanned autonomous vehicles is becoming more and more significant in recent years. The fact that the vehicles are unmanned (whether autonomous or not), can lead to greater difficulties in identifying failure and anomalous states, since the operator cannot rely on its own body perceptions to identify failures. Moreover, as the autonomy of unmanned vehicles increases, it becomes more difficult for operators to monitor them closely, and this further exacerbates the difficulty of identifying anomalous states, in a timely manner. Model-based diagnosis and fault-detection systems have been proposed to recognize failures. However, these rely on the capabilities of the underlying model, which necessarily abstracts away from the physical reality of the robot. In this paper we propose a novel, model-free, approach for detecting anomalies in unmanned autonomous vehicles, based on their sensor readings (internal and external). Experiments conducted on Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) demonstrate the efficacy of the approach by detecting the vehicles deviations from nominal behavior.

This paper describes a novel programmatic approach to efficiently distribute resources in a dynamic cellular network, using local negotiations. Our proposed mechanism is reactive and facilitates parallel self-adaptation efforts, leading to dynamics that improve overall network performance. The local nature of the negotiations being performed as part of the adaptation process enables frequent changes in the network's parameters with a negligible coordination overhead. The results of our experiments suggest rapid adjustment to changes and overall improvement over time in the number of users served by the network. We evaluate our algorithm based on the service level index, measured by the number of covered handsets. Nevertheless, the proposed algorithm supports any set of parameters and any combination of performance measures supplied by service providers.