Research in the AI Lab is in two major areas:
- Multi-Agent Modeling
- Evolutionary Computation
Agents are computational entities that can be viewed as perceiving their environment through sensors and acting on the environment through affectors. Multi-agent systems are collections interacting, intelligent autonomous agents that are in pursuit of some set of goals or need to perform some set of tasks. Distributed AI relates to the construction and application of multi-agent systems.
Multi-agent systems have become of increased interest in the past decade for the following reasons. The first reason is that multi-agent systems can be a useful modeling tool in the design of modern computing platforms which are distributed, large, and heterogeneous in nature. A second reason is that multi-agent systems can be useful in understanding and modeling theories of the evolution of complex social systems. A brief description of a current laboratory project funded by the National Science Foundation Intelligent Systems division, IIS-9907257 that relates to the latter reason is now given.
A state is among the most sophisticated and powerful structures that has emerged from the social evolution process. In the modern world these are termed "nation states’ with a government composed of a hierarchical decision-making structure where the decision-makers are either elected or appointed. States are supported by various economies and are able to interact with each other via warfare, trade, etc. Most states in the ancient world-often called archaic states-were ruled by hereditary royal families. These archaic states exhibited much internal diversity with populations numbering from tens of thousands to millions. They had a bureaucracy, organized religion, a military presence, large urban centers, public buildings, public works, and services provided by various professional specialists. The state itself could enter into warfare and trade-based relationships with other states and less complex neighbors.
The process by which complex social entities such as the state emerged from lower level structures and other supporting economies has long been of prime interest to anthropologists and other disciplines as well. This is because the emergence of such a social structure can have a profound impact on the societies’ physical and social environment. However, the task of developing realistic computational models that aid in the understanding and explanation of state emergence has been a difficult one. This is the result of two basic factors:
- The process of state formation inherently takes place on a variety of temporal and spatial scales. The emergence of hierarchical decision-making can be viewed as an adaptation that allows decision-makers to specialize their decisions to particular spatial and temporal scales.
- The formation of the state is a complex process that is fundamentally directed by the social variables but requiring dynamic interaction between the emergent system and its environment. Identifying the nature of these interactions is one of the reasons why the process of state formation is of such interest.
The goal of this project is to produce a large-scale knowledge-based computational model of the origins of the Zapotec State, centered at Monte Alban, in the Valley of Oaxaca, Mexico. State formation took place between 1400 BC. and 300 BC. While archaic states have emerged in various parts of the world, the relative isolation of the valley allowed the processes of social evolution to be more visible there. Extensive surveys of the valley were undertaken by the Oaxaca Settlement Pattern Project in the 1970’s and 1980’s. The location and features of nearly 3,000 sites dating from the archaic period (8000 BC.) to Late Monte Alban V (just prior to the arrival of the Spaniards) were documented. Several hundred variables were recorded for each site. These data are the basis for generating the knowledge used in our model. Over the past 5 years we have developed a database to house this data. We have used techniques from machine learning (genetic and cultural algorithms) and data mining to extract information about site settlement decision-making, as well as other facets of the social evolution process. The project has three phases:
Year 1: We will complete the analysis of the Oaxaca Settlement
Survey data in order to produce knowledge about warfare, trade, and economic decisions. The construction of the basic computational model of state formation will begin. The approach will center on the use of paradigms from evolutionary learning, such as Genetic and Cultural Algorithms.
Year 2: Development of a prototype model of state evolution will take place.
Since the Valley of Oaxaca consists of 5 different sub-regions, each interacting with the emerging state in different ways at different times, a distributed model is proposed here. The complexity of the model will require that each regional model runs on a separate network site, and interact with each other at different temporal and spatial scales through the network.
Year 3: Model will be used to test a various hypotheses concerning the emergence of complex systems.
These hypotheses relate to the importance of processes such as chiefly cycling, hydraulic despotism, social circumscription, consolidation of resources, and territorial expansion among others. The results of the simulations will be compared with the patterns observed in the actual site settlement data for the valley.
The laboratory has also been a focus for the development of evolution-based machine learning tools for use in modeling the cultural evolution in multi-agent systems and for data-mining of the large-scale data sets associated with such systems. Dr. Reynolds has developed Cultural Algorithms, a framework for modeling the evolution of social systems. Cultural Algorithms have been used to solve a variety of real world problems, including function optimization, knowledge base design and re-engineering, software design, in addition to the application above. Over 100 articles and two books have been produced by laboratory research in the past.