The PhD hooding ceremony was this weekend. Hrishi, Hong and Karthik took pictures, some of which I have shamelessly borrowed and placed on my own flickr set for all to see.
Putting the magic in the machine since 1980.
Thursday, August 2, 2007
The other Phd graduate this summer is Hong Jiang and her thesis is
- Hong Jiang. From Rational to Emotional Agents. University of South Carolina, 2007.
To date, most research on multiagent systems has focused on rational utilitymaximizing agents. However, theories show that emotions have a strong effect on human~s physical states, motivations, beliefs, and desires. The details have not been explicated clearly so far. In artificial intelligence, emotions have begun to receive more attention, but mostly in human-robot/computer interaction. The research on applying emotions to agents~ decision-making is still very limited. Can agents be intelligent without emotions? We believe that, whether for humanlike or non-human-like agents, the effect of emotions on decision-making cannot be ignored, since agents with high emotional quotients (EQs) can be built to have better performance in complex dynamic environments than purely rational agents. This research focuses on the effects of emotions on decision-making. Taking into account the incompleteness of emotion theories and emotional differences among individuals, I describe EBDI, a common architecture for emotional agents, which specifies a separate emotion mechanism within an agent, instead of trying to model emotion mechanisms to reflect the reasoning process specifically, like most researchers have done. It reflects the practical reasoning process, and one can select and apply part of an emotion theory into the architecture as needed. Sample agents in Tileworld are presented and the results show that an EBDI agent can have better performance than traditional BDI agents. To apply EBDI in negotiation, a plug-in is designed, which modifies the OCC model, a standard model for emotion synthesis, to generate emotions. Considering the possibility of incorporating emotions into negotiation, I generate EWOD (EmotionalWorth- Oriented Domain), which requires numerical emotions. Thus, a mapping from 22 OCC emotions to 3-dimension numerical PAD emotions is given. Finally, I describe how PAD emotions affect the negotiation strategy and provide an evaluation which shows that it can be used to implement emotional agents that mimic human emotions during negotiation. Thus we can design high EQ agents for negotiation according to specific design purposes. Since negotiation is used widely in many different domains, this research, based on a general process of negotiation, can also be widely applied to other areas.
Congratulations are also in order for Hong. Her research is highly innovative, crossing boundaries between computer science, cognitive science, and Sociology. She has shown how simulated emotions can be incorporated into negotiating agents for the betterment of the whole agent society, as well as how agents can be made to behave like normal irrational humans. Check our her extensive list of papers. She will be joining the faculty at Benedict College.
We have two new PhD graduates this summer. One of them is Hrishikesh Goradia who has successfully defended his PhD thesis:
- Hrishikesh J. Goradia. Automated Negotiation Among Autonomous Agents in Negotiation Networks. University of South Carolina, 2007.
Distributed software systems are a norm in today~s computing environment. These systems typically comprise of many autonomous components that interact with each other and negotiate to accomplish joint tasks. Today, we can integrate potentially disparate components such that they act coherently by coordinating their actions via message exchanges. Once this integration issue is resolved, the next big challenge in computing is the automation of the negotiation process between the various system components. In this dissertation, we address this automated negotiation problem in environments where there is a conflict of interest among the system components. We present our negotiation model - a negotiation network - where a software system is a network of agents representing individual components in the system. We analyze the software system as a characteristic form game, one of many concepts in this dissertation borrowed from game theory. The agents in our model preserve the selfinterest of the components they represent (their owners), and make decisions that maximize the expected utilities of their owners. These agents accomplish joint tasks by forming coalitions. We show that the problem of computing the optimal solution, where the utilities of all agents are maximized, is hyper-exponential in complexity. We present an approximate algorithm for this hard problem, and evaluate it empirically. The simulation results show that our algorithm has many desirable properties - it is distributed, efficient, stable, scalable, and simple. Our algorithm produces the optimal (social welfare maximizing) solution for 96% of cases, generates maximal global revenue for 97% of cases, converges to 90% of the best found allocation after only 10 rounds of negotiation, and finds a core-stable solution for revenue distribution among the agents for cases with nonempty core. Finally, to ensure stability for all cases, we present a sliding-window algorithm that computes the nucleolus-stable solution under all situations.
Congratulations are in order for Hrishi! His thesis presents some highly innovative algorithms for automated negotiation, a topic that I expect will see many applications in the near future as semantic we technologies come of age and automated service orchestration becomes the norm in business processes. Go read it! I also want to point out that Hrishi's research contributions go far beyond his thesis; many of Hrishi's publications did not even make it into his thesis. He has accepted a position in the computer science department at Western Carolina University. We wish him well.