Use Of Artificial Agents Research Articles

The effect of anime character\u2019s facial expressions and eye blinking on donation behavior

Studies of the use of artificial agents and robots to solicit donations from people have suggested that the design of the agents must consider facial expressions. However, there has not been sufficient evidence to generalize the finding that the emotions conveyed by agents’ facial expressions can induce donations. In the present study, we conducted an experiment with an animated character that has intermediate realism and a different appearance from those in previous studies to replicate the finding that facial expressions represented by changes in the shapes of the eyes and mouth cause people to become more prosocial and to test whether we can extend this finding to the emotional expressions presented by changes in the dynamic properties of eyes. In the experiment, participants (n=100) played a hypothetical dictator game with an avatar that expressed its emotions by changing the shapes of its eyes, eyebrows and mouth and by changing the frequency of eye blinking. The results showed that the emotions expressed by changes in the shape of the facial parts contributed to eliciting a higher donation amount, consistent with previous studies. However, we could not find an additive effect of the emotional expression shown by eye blinking. The results suggest that, regardless of appearance, emotional expression is useful in the design of a virtual agent’s face, but it might not be necessary to consider the dynamic properties of the eyes.

Artificial agents among us: Should we recognize them as agents proper?

In this paper, I discuss whether in a society where the use of artificial agents is pervasive, these agents should be recognized as having rights like those we accord to group agents. This kind of recognition I understand to be at once social and legal, and I argue that in order for an artificial agent to be so recognized, it will need to meet the same basic conditions in light of which group agents are granted such recognition. I then explore the implications of granting recognition in this manner. The thesis I will be defending is that artificial agents that do meet the conditions of agency in light of which we ascribe rights to group agents should thereby be recognized as having similar rights. The reason for bringing group agents into the picture is that, like artificial agents, they are not self-evidently agents of the sort to which we would naturally ascribe rights, or at least that is what the historical record suggests if we look, for example, at what it took for corporations to gain legal status in the law as group agents entitled to rights and, consequently, as entities subject to responsibilities. This is an example of agency ascribed to a nonhuman agent, and just as a group agent can be described as nonhuman, so can an artificial agent. Therefore, if these two kinds of nonhuman agents can be shown to be sufficiently similar in relevant ways, the agency ascribed to one can also be ascribed to the other--this despite the fact that neither is human, a major impediment when it comes to recognizing an entity as an agent proper, and hence as a bearer of rights.

Choosing the best city of the future

This paper describes various possibilities of the cities of futures considering various constraints and demand of society, environment and geography. The need for future cities arises because of the rapid growth in population and thereby causing a decline in the living standards. In the United States itself, many people are moving to cities every day. Today cities are getting crowded and if the influx continues at the same rate, current cities will become unmanageable and unlivable.More population means faster consumption of natural resources which eventually leads to ecological imbalance. Already more than 80% of world׳s forests are gone. The food consumption has also doubled over the last fifty years and this has exerted pressure on the landscape through the use of artificial fertilizers. The use of artificial agents to boost agricultural productivity has significantly marred the flora and fauna leading to loss of a huge amount of bio diversity.Another problem with the increasing population and population migration to cities is the rise of congestion on the city roads. In the United States a person drives 7500 miles in 1600h with an a average speed of 4.68mph. To accommodate the population approximately 2000 trees are cut in a minute in the Amazon alone.On top of all that, with increasing wealth the average house size has almost doubled since 1970 and this has further caused the decline of arable land and forests. To deal with the problem of rising population and congestion within the cities, we need to plan cities of the future that will be able to utilize the available resources in a more efficient and cleaner manner. The future city project aims to delve into details of various future city models and aims to find out which model will be best suitable depending upon the strategic criteria that we have used to evaluate the various merits of the BOCR model.The four alternative kinds of cities are analyzed below according to their merits. They are A. Compact City, B. Elevated City, C. Green House City, and D. Water City.

Reverse Auctions with Multiple Reinforcement Learning Agents*

ABSTRACTReverse auctions in business‐to‐business (B2B) exchanges provide numerous benefits to participants. Arguably the most notable benefit is that of lowered prices driven by increased competition in such auctions. The competition between sellers in reverse auctions has been analyzed using a game‐theoretic framework and equilibria have been established for several scenarios. One finding of note is that, in a setting in which sellers can meet total demand with the highest‐bidding seller being able to sell only a fraction of the total capacity, the sellers resort to a mixed‐strategy equilibrium. Although price randomization in industrial bidding is an accepted norm, one might argue that in reality managers do not utilize advanced game theory calculations in placing bids. More likely, managers adopt simple learning strategies. In this situation, it remains an open question as to whether the bid prices converge to the theoretical equilibrium over time. To address this question, we model reverse‐auction bidding behavior by artificial agents as both two‐player and n‐player games in a simulation environment. The agents begin the game with a minimal understanding of the environment but over time analyze wins and losses for use in determining future bids. To test for convergence, the agents explore the price space and exploit prices where profits are higher, given varying cost and capacity scenarios. In the two‐player case, the agents do indeed converge toward the theoretical equilibrium. The n‐player case provides results that reinforce our understanding of the theoretical equilibria. These results are promising enough to further consider the use of artificial learning mechanisms in reverse auctions and other electronic market transactions, especially as more sophisticated mechanisms are developed to tackle real‐life complexities. We also develop the analytical results when one agent does not behave strategically while the other agent does and show that our simulations for this environment also result in convergence toward the theoretical equilibrium. Because the nature of the best response in the new setting is very different (pure strategy as opposed to mixed), it indicates the robustness of the devised algorithm. The use of artificial agents can also overcome the limitations in rationality demonstrated by human managers. The results thus have interesting implications for designing artificial agents in automating bid responses for large numbers of bids where human intervention might not always be possible.

Successful continuation of pregnancy after repair of a midgestational uterine rupture with the use of a fibrin-coated collagen fleece (TachoComb) in a primigravid woman with no known risk factors

We report the first case of successful continuation of pregnancy after repair of a midgestational uterine rupture with the use of a fibrin-coated collagen fleece (TachoComb, Nycomed, Linz, Austria). For midgestational uterine rupture, adequate uterine repair and close surveillance of preterm labor could improve perinatal outcome by permitting continuation of the pregnancy.

Autonomous Agent Models of Stock Markets

The use of artificial agents in the study of stock markets has aroused much interest in the past two decades. Models of markets consisting of agents were built to reinforce or question theories in economics – including the principle of ``negative feedback'', the Efficient Market Hypothesis, and chaos theory. In this article, we review the development of these agent models, highlight key design issues and problems, and suggest some directions for future research.