A GeoAgent-based Framework for Knowledge-oriented Representation: Embracing Social Rules in GISystems

Transcription

1 A GeoAgent-based Framework for Knowledge-oriented Representation: Embracing Social Rules in GISystems Chaoqing Yu Department of Water Hazard Research China Institute of Water Resources and Hydropower Research (IWHR) A1 Fuxing Road, Haidian District Beijing, P.R. China Donna J. Peuquet Department of Geography The Pennsylvania State University 302 Walker Building University Park, PA, USA Acknowledgements We wish to acknowledge the joint support from the Human-Environment Regional Observatory (HERO) project by the National Science Foundation and the National Oceanic and Atmospheric Administration (NSF Grant SBE , Brent Yarnal, Principal Investigator). We are grateful for the insightful comments from Brent Yarnal, Alan MacEachren, Mark Gahegan, and the anonymous reviewers. Thanks also go to the help from David O'Sullivan, Junyan Luo, Xinhua Han, and other members in the GeoVISTA Center and the HERO research team. Human subject protection for social science research is under IRB No at the Pennsylvania State University.

2 A GeoAgent-based Framework for Knowledge-oriented Representation: Embracing Social Rules in GISystems Abstract: While current GISystems (geographic information systems) can represent observational spatial data well, they have limited capabilities in representing some non-observational social elements and goal-driven behaviors that can be important factors in a wide range of geographic issues. Such social elements and behaviors can include laws, regulations, polices, plans, culture and customs, as well as their relations and interactions with the geographic environment at different scales. Getting beyond traditional data-centered approaches, this research presents a knowledgeoriented strategy in order to address these issues within a GIS context. We incorporate two major conceptual elements. First, extending from conventional agent notions and their geographic applications, geographic agents (GeoAgents) are considered as a basic representation component to specifically address social and goal-driven behaviors that impact the Earth and environmental systems, as well as to represent higher-level knowledge. Second, in order to incorporate GeoAgents with current space-time representation, a new conceptual representation framework, called FOTAR, is introduced to address the cross-scale processes of both social and natural interactions. A Java-based prototype, GeoAgent-based Knowledge System (GeoAgentKS), is described to implements this framework by integrating agent technologies with multiple data and knowledge representation techniques, such as expert systems, concept maps, mathematical models, and geospatial databases. The application of this prototype in a case study is also presented, investigating scale-dependent human-environment interactions under different emergency situations for CWSs (community water systems) in Central Pennsylvania, USA. In this case study, a systematic set of 1

3 methodologies of knowledge acquisition, representation, and confirmation for constructing GeoAgents knowledge bases by using expert systems were explored to formalize high-level knowledge and social behaviors in the FOTAR-based representations. The results show that the proposed conceptual representation framework is achievable at both implementation and application levels, and the prototype tool is demonstrated to be valuable in facilitating knowledge sharing, policymaking, municipal management, and decision-makings, especially for the real-world emergency management. Key words: representation, GeoAgents, social rules, human-environment interactions, expert systems, emergency management 2

4 1. Introduction Geographic representation plays a central role in establishing correspondences between theories and the world (Raper 1999), and in guiding human actions within the world (Peuquet 2002). Beyond the vector vs. raster debate, GIScientists have made fundamental progress in geographic representation after the late 1980 s. The conceptual level object vs. field view (Goodchild 1989; Couclelis 1992; Peuquet 2001; Cova and Goodchild 2002) is now widely accepted as the basic framework for representing the discrete and continuous phenomena in the world. Representing time within GISystems has also gained significant attention, with a number of space-time data models being proposed (e.g., Langran and Chrisman 1988; Langran 1989; Peuquet 1994; Yuan 1994; Raper and Livingstone 1995; Couclelis 1999; Wachowicz 1999; Christakos 2002; Peuquet 2002). Due to these contributions, object, field, and time in GIScience have been widely accepted as the three fundamental components for representation of observed environmental dynamics. In spite of such progress, representation of the geographic world in GISystems is still datacentered. This conforms to the standard scientific method whereby analysis is based (and hypotheses proven or disproven) on objective, observed facts (i.e., data). Nevertheless, there is no representation component in the object-field or object-field-time representation scheme to specifically represent non-observational social elements and goal-driven behaviors that can be important factors influencing a wide range of geographic phenomena. Such social elements and behaviors can include laws and regulations, policies, culture, customs and goals. Moreover, there is little capability to represent abstracted, higher-level knowledge of how a given phenomenon works. Because the interactions among social and natural factors in the Earth system are diverse, spatially distributed, and scaledependent, it is impossible to use any monolithic representation to fully address such complexity. The 3

5 development of agent technologies in recent years provides a means for dealing with this complexity (also see Manson and O'Sullivan 2006; Evans and Manson 2007). While agent technologies are not new to GIScience, in most geographic applications, agents have been employed generally as a computational modeling technique for simulation or GIS software engineering. The research reported in this paper attempts to go beyond this to consider agents as a basic component, allowing social and goal-driven behaviors to be incorporated in changing environments. This capability is particularly important for ad-hoc analysis and decision-making in emergency situations. We call this use of agents within a geographic context GeoAgents (or geographic agents). Rather than data-centered approaches, the primary purpose of this paper is to introduce a conceptual framework, called FOTAR, to integrate GeoAgents with object, field, and time for knowledge-oriented representation of the interrelations or interactions of both social and natural elements involved in geographic systems. Using FOTAR as a basic representation scheme, this paper illustrates ways to approach the representations of diverse social rules and hierarchical humanenvironment interactions in knowledge-oriented GISystems. To implement this conceptual framework, a Java-based prototype called the GeoAgent-based Knowledge System (GeoAgentKS) was developed. Employing this prototype tool, a case study was conducted utilizing CWS (community water systems) emergency management in Central Pennsylvania (PA) as an example. In this demonstration, GeoAgents were used to represent the governmental, institutional, and individual behavioral rules in their expert-system-based knowledge bases, as well as their interactions with multi-scale geographic environments. The knowledgeengineering methodologies for knowledge acquisition, representation and evaluation, and the ways of integrating knowledge-representation, including expert systems and concept maps (Hughes and Hay 2001; Novak 1990) with geospatial databases were also employed in the case study. 4

6 In the remainder of the paper, section 2 below provides a brief contextual background, focusing on the agent-based approaches and expert systems in geographic applications. Section 3 discusses the concept of GeoAgents, presents the FOTAR framework, and demonstrates a FOTARbased hierarchical architecture for representing scale-dependent human-environment interactions in a brief example. Section 4 introduces the Java-based prototype, GeoAgentKS. Section 5 explores the knowledge-oriented strategy and the detailed knowledge-engineering methodologies for building the FOTAR framework in GeoAgentKS by using extant laws and regulations regarding emergency water management in Central Pennsylvania. The results and methods for validating GeoAgent behaviors are also discussed in this section. The final section provides conclusions and outlines a set of research topics for future study. 2. Agents and expert systems in geographic applications: a brief background What an agent is, exactly, remains a topic of debate, but they do have a number of essential characteristics. In the most general terms, an agent is an entity that is situated its environment, can act autonomously upon that environment, and can react to changes in that environment (Murch and Johnson, 1999). An agent is also goal-oriented, can plan and learn, and can interact with other agents (i.e., has social ability). Agent-based systems has become an important strategy for distributed problem solving, particularly with the advent of the World Wide Web and distributed processing, as well as for providing a practical divide-and-conquer approach for dealing with multi-faceted problems. Expert systems can be generally characterized as software systems that imitate the problemsolving capability of an expert within some specific domain. Expert systems have adopted agentbased approaches, particularly distributed knowledge representation, as a means to deal with broader problem domains as well as changing contexts. Using expert systems to support agent-based 5

7 cooperative activities can be traced back to the early 1990s in the field of AI (artificial intelligence). For example, Jennings et al. (1993) slightly modified some pre-existing expert systems and integrated them with agents to diagnose faults that occurred in the real particle accelerator process. 2.1 Geographic applications of agents Perhaps the best known use for an agent-based approach in GIScience is as a computational modeling and simulation technique, oftentimes called agent-based modeling (ABM), for simulating dynamic geographic phenomena. In such applications, behavioral rules of agents usually are determined by the status (e.g., density) of their neighboring agents or local environmental conditions (e.g., in form of grid cells) through the use of simulation (e.g., mathematical) models. Before the appearance of the "agent" concept, similar approaches relevant to simulation of geographic phenomena had already been in existence, e.g., cellular automata (Couclelis 1985, Itami 1988). Since the 1990s, the ABM techniques have been utilized for a range of purposes, e.g., for simulating dynamic societies (Epstein and Axtel 1996), watershed dynamics (Batty and Jiang 1999), pedestrian movement (Schelhorn et al. 1999), wildlife dynamics (Westervelt and Hopkins 1999), housing segregation (Sanders et al. 2000), path finding (see Frank et al. 2001), land use and land cover change (LUCC, e.g., Manson 2000; Parker, et al. 2001; Parker, et al. 2003, Evans and Kelley 2004), panda protection (An et al. 2005), and influenza pandemic (Ferguson, et al. 2006). In addition, there have been a variety of agent-based tools (e.g., Repast ( NetLogo ( and Swarm ( developed to allow such a modeling technique to be more easily employed in different application contexts. Although the purposes of these applications are diverse, the basic simulation methodologies are similar. The environmental conditions (e.g., land use, distance, slope, population, etc.) of individual agents are generally weighted and quantified in mathematical models (or other relatively simple behavioral 6

8 rules) to affect their behaviors in simulating overall dynamic patterns, self-organization, emergence, and evolution in geographic systems. Distinct from the above simulation applications, agents have also been used to provide smarter solutions in a GIS environment for improving the functionality of GISystems. Here, agents act upon the internal software environment. Rodrigues and Raper (1999) envisioned this type of application early-on and described spatial agents that could be used to perform spatial data mining, improve GIS interfaces, facilitate spatial analysis, connect different GIS systems, and share data and information over networks. Similarly, Wu et al., (1999) used the term GeoAgents and believed that their agent-based approach could be used to assist spatial information retrieval, knowledge discovery, pattern recognition, and model assessment. Tsou and Buttenfield (2002, and used an agent-approach (also called GeoAgent on the website) for Distributed Geographic Information Services. Similar agent-based approaches have also been applied to web-accessible repositories of spatial data and models for spatial decision support (e.g., Sengupta and Bennett 2003, Nute et al. 2004). 2.2 Expert systems for GIS Some researchers have utilizes expert systems in GIS to encode domain knowledge for facilitating data analysis, information management, and operation of GISystems. Examples include automation of map design (Su 1996); knowledge-based data search and information query for soilerosion mitigation (Ross 1993), forest resource (Loh et al. 1994), pastoral agriculture (Tiangang et al. 2004), and landfill management (Lukasheh et al. 2001); and automated drought data interpreting and monitoring (Smith 1998). Similar purposes of applications can also be seen in (Chuenpichai et al. 2002; Leung and Leung 1993; Smith et al. 1987). Nevertheless, rarely have researches been reported in GIS contexts to integrate expert systems with agent technologies for representing the structured 7

9 social laws and rules, building inter-agent communication mechanisms, and simulating scaledependent human-environment interactions. In the remainder of this paper, we first introduce our view of GeoAgents and the conceptual FOTAR framework. We then describe how we integrate expert systems with agent and other technologies to achieve a prototype implementations and real-world application of this framework. 3. The FOTAR representation framework This section introduces the notion of geographic agents, the conceptual FOTAR representation framework, and a brief example to illustrate how to use this framework to represent scale-dependent human-environment interactions. 3.1 From objects and agents to GeoAgents As discussed in the previous section, agents have primarily been utilized in GIScience contexts as a computational tool for simulation, or a technique for improving GIS functionality. In contrast, Luck and d Inverno (2001) consider agents as something beyond a tool or technique. They see agents as a fundamental component that comprises our view of the world. From a computerscience perspective, they present a hierarchical framework to represent the world, which consists of entities, objects, agents, and autonomous agents. An entity is a collection of attributes; an environment is a collection of entities; an object is an entity with a set of actions; an agent is an object with goals; an autonomous agent is a self-motivated agent that pursues its own agenda rather than being under the control of other agents. Luck and d Inverno assert that having goals is the minimum requirement for an entity or an object to be considered an agent. Similarly, Zambonelli et al., (2003) believe that agents are characterized by autonomy, situatedness, and proactivity, but objects are not. The motions of objects generally are passively driven by external forces rather than by their internal motivations. 8

10 From a GIScience perspective, as mentioned earlier, geographic space is beheld with an object-field view to address the discrete (e.g., buildings, roads, and rivers, etc.) and continuous (e.g., air pressure, and temperature, etc.) world. In this view, nevertheless, there is no representational component to particularly represent things with internal goals and social behaviors, and the complex interactions among both social and natural components in the geographic world. To tackle these issues, we suggest adding an agent view to the existing object-field-time representations in GIS. This view can be labeled as geographic agents or GeoAgents. As an extension of the object-field-time view, GeoAgents are a basic representation component that designates a special class of objects in the geographic world, which have goal-driven behaviors. In this context, if an object has rules and behaviors but without goals, it is not a GeoAgent. For example, a falling rock is considered as an object with motions. Even though the rock s motions may follow some rules, it is still not a GeoAgent because the motions are passively driven by external forces (e.g., gravity) rather than by its internal goals or motivations. In the Earth system, animals such as ants and birds, human individuals, and social organizations, etc. can be considered as GeoAgents because they usually interact with particular geographic environments, and their behaviors are oftentimes driven by goals (e.g., seeking food or migration). Also, as described in the framework by Luck and d Inverno, GeoAgents can be autonomous, may have their own internal agenda, and can plan their tasks to reach goals. They can be social, can interact with each other, can serve other GeoAgents, or can be served. These goal-driven, autonomous, and social characteristics (also see Jennings, et al. 1998) make GeoAgents special objects in representing our geographic world. GeoAgents are viewed here on a conceptual level. And as such, they are not constrained by any specific technologies or methodologies of how their knowledge and environments being represented, or how to access geographic databases or knowledge representations. Their behavioral rules can be 9

11 implemented as mathematical models, qualitative rules (e.g., in expert systems), or whatever method seems appropriate. GeoAgents are geo because they interact with scale-dependent geographic environment in given contexts and have the ability to reason spatially. For example, when developing GeoAgentbased GISystems in practice, the user can design specific spatial-analysis functionalities (e.g., spatial statistics, map algebra, distance decay, direction) for GeoAgents, and to give GeoAgents the ability to output their decisions or execute the results geographically (i.e., output a map, and notify other agents in various locations, etc.). Many agents developed in computer science or other fields would not be considered GeoAgents because they do not interact with an explicitly geographic environment. For instance, some agents are designed to improve systems, Internet search engines, stock market management, and manufactory productivity, etc. and operate within a given space. They are not GeoAgents because they consider computer hardware, network speed, Internet websites, business data flow, or manufactural devices as their environments, which typically do not have the same characteristics as geographic environments To summarize, the following four criteria generally can be used to characterize a GeoAgent: (1) it is a representation component (i.e., designating something in the geographic world); (2) it has goals; (3) it interacts with a geographic environment; and (4) it is used in a geographic application context. These criteria only provide a proximate rather than a clear-cut definition of what are (or are not) GeoAgents, because oftentimes it is difficult to tell what is geographic or non-geographic. Furthermore, as discussed by Luck and d Inverno (2001), agents sometimes can be considered ordinary objects, and vice versa. The more important issue of introducing the GeoAgent as a representational type is to allow representation and analysis of social laws and rules, cultural elements, the structured human societies, social behaviors, and the processes of human-environment interactions in GISystems. Geo primarily refers that the development of GeoAgent-related theories 10

12 and technologies needs to meet geographic requirements (especially focusing on human-environment relations), rather than for other purposes of agent applications. 3.2 FOTAR Incorporating GeoAgents with current space-time representations, we consider field, object, time, and GeoAgent as four fundamental representation components to address both social and natural elements in the dynamic geographic world. High-level knowledge about human understanding of the relations and/or interactions among these elements is considered to be a representation of geographic processes in given contexts. Inspired by the pyramid framework (Mennis, et al. 2000, also see Peuquet 1988; Peuquet 1994; Peuquet 1999), a new representation framework, FOTAR (fields, objects, time, GeoAgents, and relations), is introduced here for representing the processes of both social and natural interactions involved in the Earth system. GeoAgent Relations Object Field Time Figure 1: A FOTAR (Field, Object, Time, GeoAgent, and Relations) representation framework (left) and its symbol (right) 11

13 As shown in Figure 1, the FOTAR framework includes the object-field view, a time component, as well as the GeoAgent view. The goal-driven and social rules can be stored within GeoAgents to drive social behaviors and social-natural interactions involved in the world. The central part of this framework is the relations component, which represents high-level understanding of the relationships (e.g., taxonomic (kind-of) and partonomic (part-of) relationships) among the geographic elements (i.e., fields, objects, GeoAgents), the cause-and-effect chains, and the mechanisms of how these elements interact within the Earth system over time. In addition to data, therefore, achieving the FORAR-based geographic representation requires a knowledge-oriented strategy. Figure 2: A FOTAR-based hierarchical architecture (left) within a space-time-scale coordinate system (right) for the representation of geographic processes (E: EPA, D: DEP, C: CWS, P: power company, F: fire department, m: water manager, o: water operator; u: water users) E Scale E D D D... D E D D... D... D C C C... Time F C P C... C C C C... C m o u u... m o u... T2 m o o u u... m u u u T1 Space (a) (b) Using FOTAR as a basic unit, a hierarchy (Figure 2) can be constructed to represent the scaledependent processes of human-environment interactions. As discussed earlier, the behavioral rules of GeoAgents can be derived from social laws, regulations, policies, religions, customs, plans, beliefs, scientific knowledge, individual goals and intentions, as long as they can affect the geographic 12

14 environment. GeoAgents can store social rules and institutional regulations at varying levels, and such rules and regulations usually correspond to specific spaces at given scales. For instance, a federal-level law is valid for the entire country, but some state (province) level regulations only valid within the particular states (provinces). The scale-dependent interrelationships can be represented in a graph, or a set of graphs, as being previously suggested by other geographers and GIScientists (e.g., Leitner 2004; McMaster and Sheppard 2004; Taylor 2004). Figure 2 shows the FOTAR units in a graph-based hierarchy for representing a process of scale-dependent human-environment interactions by using management of community water systems in the US as an example scenario. GeoAgents can be used to represent particular social agencies, such as the EPA (i.e., the federal level Environment Protection Agency), the DEPs (i.e., the state level Departments of Environment Protection), local CWSs (community water systems) and other local institutions (e.g., fire departments and power companies), and individual water managers or water users. These GeoAgents have their own independent knowledge bases that store the different institutional levels of laws, regulations, plans, or behavioral rules. For example, the EPA provides nation-wide laws and standards for safe drinking water. A DEP inherits these national standards, makes more detailed regulations and plans at the state level, and monitors the water management of local CWSs within that state. In addition to following higher-level regulations, each CWS has its own regulations and plans to interact with its local environment so as to supply stable and safe drinking water to individual water users. Therefore, social goals (e.g., water standards) can be inherited from higher levels within the hierarchy, specified or modified at different levels, and eventually embodied in individual water users daily life. Social interactions, as those noted above, are interrelated with complex physical processes. For instance, drought, flood, pollution, geology, land use and land cover changes can significantly affect water quantity and quality in different scales and different places, and result in cross-level 13

15 human responses. These physical factors can be represented using geographic databases and scientific models. To integrate hierarchical social components with their physical environments, a data environment to which each GeoAgent can connect needs to be established. For example, the EPA GeoAgent interacts with nation-wide geographic data; the DEP GeoAgents interact with their statewide data; and local CWS GeoAgents interact with their corresponding local data. Based on their internal rules, GeoAgents are "aware" of their environmental conditions by retrieving information from the database, performing spatial analysis, or communicating with each other. In the CWS example, once a drought emergency is identified in a particular region via interpreting the statewide environmental data, the DEP GeoAgents will inform the local CWSs in the affected regions. Each CWS GeoAgent in these regions will respond by activating its own drought contingency plan, monitoring its local water sources in its data environment, and informing its individual water users to conserve water use during the drought condition. It is important to note that geographic components represented in the FOTAR-based hierarchical architecture are dynamic over space, time, and scale (Figure 2 (b)). For example, the count of the water users within a CWS can increase or decrease; old wells are subject to abandonment, and new wells are drilled in somewhere else; small CWSs can grow into large water companies, or may disappear. Social regulations and laws can be enacted, amended, or abrogated. More importantly, such social and natural changes may result in a new pattern of dynamic interactions in the geographic system represented. Therefore, as shown in Figure 2 (b), the FOTARbased hierarchical architecture exists within a space-time-scale coordinate system for representing the complexity of dynamic geographic processes. To reflect an accurate sense of the world, time representation should be carefully considered. When a GeoAgent is used to represent a CWS in year 2001, its actions should be based on a social background that is feasible in Its physical environment (e.g., land cover, geology, water 14

16 sources, weather) should also be based on the state of the environment in this year. But in 1990, for example, the social background and physical environment could be significantly different. Time representation therefore must address not only the changing phenomena in geographic databases, but also the dynamic social interactions. Time representation in the distributed knowledge/data representation of the FOTAR framework also needs to be congruous. For example, communication between a GeoAgent in 2001 and another GeoAgent in 1990 cannot be allowed in order to appropriately reflect the reality. In addition, time representation should also be considered for discovering unknowns, projecting future states, dealing with feedback, and for simulating emergence and evolution. Nevertheless, a holistic solution for presenting time in this framework still remains a challenge, and will become a significant research direction in future study. To implement the FOTAR framework, a Java-based prototype tool, called the GeoAgentbased Knowledge System, was developed. This was then employed to represent the emergency watermanagement processes in Central Pennsylvania, USA, as in the scenario described above. The next section introduces the technical details of this prototype, and Section 5 presents the watermanagement example. 4. GeoAgent-based Knowledge System (GeoAgentKS) 4.1 Technologies for implementation The processes of human-environment interactions are complex. It is difficult to use any single technique to fully represent such complexity. As pointed out by Minsky (1991), to solve really hard problems, several different representations must be used. This philosophy and approach is reflected in implementation of the FOTAR representational framework. And beyond the need for multiple representations, the specific mix of representational schemes best employed may vary from one application context to the next. For FOTAR, the field and object components within the framework 15

17 can be represented as either raster or vector geographic databases, or both. Temporal components can be represented as a series of states or a series of events. For GeoAgents, encoding their lifecycles, knowledge, organizations (e.g., teams), and communication similarly needs to consider multiple knowledge representation techniques. The behaviors of GeoAgents, as discussed in the previous section, can be expressed in either in quantitative form (e.g., simulation models) or as qualitative rules (e.g., IF THEN rules). The relations component can also be represented as quantitative (e.g., dependent and independent variables in mathematical models), or qualitatively (e.g., in graphbased concept maps, frames, or semantic networks). Various agent communication languages can also be considered, e.g., KQML (Knowledge Query and Manipulation Language, see Finin et al and and the FIPA ACL (Foundation for Intelligent Physical Agents, Agent Communication Language, Open source tools The GeoAgentKS prototype described here was not started completely from scratch. Instead, multiple pre-existing open-source software packages were adapted for facilitating implementation, which contain the desired representation techniques and methods for utilizing them. The specific packages used include JESS, MadKit, GeoTools, and Touchgraph. JESS (Java-based Expert System Shell, is used in this research as a rule engine (Friedman-Hill 2004) for driving GeoAgents behaviors. MadKit (a Multi-agent Development Kit, is utilized to manage GeoAgents' life cycles, roles, groups, and message passing. Touchgraph ( provides a graph-based concept map for representing the relations among geographic elements. GeoTools ( has the capabilities of operating and displaying spatial information in the standard raster-based and vectorbased spatial database, which can store GeoAgents' environmental conditions. The FIPA ACL (Agent Communication Language) is used in this prototype for inter-geoagent communication. 16

18 The basic approach for integrating the above diverse technologies into the GeoAgentKS prototype uses OOP (object-oriented programming) in Java. For example, a concept node in the prototype is inherited from a general concept-node class (i.e., having links and other attributes, such as name, color, shape, value, etc.) defined in Touchgraph, and extended utilizing functions within GeoTools for querying and operating on geographic databases. When a concept node designates a GeoAgent, it also points to the JESS file of this GeoAgent s knowledge base and the data files used for its environmental database. A GeoAgent is inherited from the agent class defined in the MadKit, which is an object whose lifecycle is managed by using multiple threads. The simplest agent in MadKit is an active communicating object that plays roles within groups (more agent functionalities defined in MadKit can be seen in GeoAgent in GeoAgentKS, however, can be much more complex than that, because it has access to both the JESS knowledge base and the geographic database defined for the relevant concept node. The user can upload a stored concept map as an XML file into GeoAgentKS, and then initialize a GeoAgent s lifecycle from the concept map. When a GeoAgent is initialized, its knowledge base and geographic environment are simultaneously loaded, and both the JESS inference engine and GeoTools are activated to allow this GeoAgent to use its knowledge representation to interpret its environmental conditions. The user can also define a set of concept nodes (that can be linked to databases) as environmental elements for the GeoAgents. When the environmental conditions are retrieved from the concept map and/or the spatial databases and are asserted to the JESS inference engine, the GeoAgents can use their internal knowledge via automated reasoning to generate responses. 17

19 4.3 A FOTAR-based hierarchical architecture in GeoAgentKS Figure 3: A part of the GUI of GeoAgentKS Figure 3 shows a part of the GUI (Graphical User Interface) of the GeoAgent-based Knowledge System (GeoAgentKS). As seen in this screen image, knowledge of human understanding of the relationships among the relevant geographic elements can be represented in the concept map (upper left). The county-level spatial map (lower left) is attached to the concept map and displays the study area (i.e., Centre County in Pennsylvania) in this research. There are two running FOTAR units (right) being displayed, including the GeoAgent of CollegeTownship_CWS (i.e., College Township Water Authority) and the GeoAgent of DEP_PA (i.e., the Pennsylvania Depart of Environmental 18

20 Protection). Each FOTAR unit has a private GUI, which consists of a text window and a map window. The text windows show the actions (or executing results) of rule firing from the GeoAgents' internal knowledge bases. The map windows display two levels of data environments with which the GeoAgents interact. For example, the DEP_PA GeoAgent interacts with a statewide data environment, and CollegeTownship_CWS with a local data environment. These GeoAgents also send ACL messages to communicate with each other to allow social interactions to be possibly represented within GeoAgent KS. If there are multiple different levels of FOTAR units being interconnected in the graph-based concept map, a FOTAR-based hierarchical architecture in Figure 2 can be constructed within GeoAgentKS. As mentioned earlier, in this prototype, the concept map plays a pivotal role in integrating knowledge representation with geographic databases. When a concept node designates a GeoAgent in a FOTAR unit, this concept node is linked to this GeoAgent's knowledge base and environmental database. For example, the concept nodes "CollegeTownship_CWS" and "DEP_PA" in Figure 3 are linked to the JESS rule files and the spatial data files corresponding to these two GeoAgents. From the GUIs of the FOTAR units, therefore, the user can simultaneously view the GeoAgents actions (or executing results, see Figure 3 for the text-based outputs) from their knowledge bases and the spatial information in the maps. A concept node can also represent an event (e.g., a drought) or a feature (e.g., a well). The status of the concept node can be calculated from a model (e.g., a drought model), queried from the database (e.g., depth of the well), or input from the user. If calculated from a model, the concept node points to the definition of this model, in which the variables (e.g., precipitation or soil moisture) are linked to the observed data in the database. If the database stores a set of time serial data, the modeling outputs can be considered as a simulation of the dynamic environmental changes (e.g., the drought development from start to end). The GeoAgents are aware of their environmental conditions via interpretation of the stored concept map, the modeling outputs, or the discovered 19

21 spatial patterns. To increase the capabilities of GeoAgents in automated geographic analysis, it would be important to extend GeoTools with many additional spatial analysis tools and approaches, such as GAM (Geographical Analysis Machine, see Openshaw et al. 1987), spatial statistics, and the recent data mining techniques, for better interpretation of environmental changes. To support decision-making, the actions of GeoAgents here are defined as suggestions to the user about what to do. For example, actions (see the text outputs) in Figure 3 are the GeoAgents suggestions on the appropriate steps for DEP_PA and CollegeTownship_CWS to cope with a drought emergency. The GeoAgentKS prototype, in summary, has the following characteristics: First, rather than primarily using mathematical models, GeoAgentKS employs expert systems for knowledge representation to allow qualitative rules to be used for driving GeoAgents behaviors. Second, each individual GeoAgent s knowledge base in GeoAgentKS can be very complex, and may include multiple sets of social laws, regulations, and plans. In different contexts (e.g., drought, or flooding), GeoAgents may be interested in different environmental conditions and use different knowledge to produce responses. Third, GeoAgents individual knowledge bases and spatial data environments are relatively independent. As such, diverse and scale-dependent geographic interactions can be represented. Fourth, the concept map plays not only a central role to functionally integrate data and knowledge representation technologies, it also allows the users to flexibly define their own understanding of the relations among the involved geographic components, to test GeoAgents behaviors by changing the environmental conditions, and to enhance knowledge visualization with spatial information. 20

22 5. The case study To validate the feasibility of using GeoAgentKS for the FOTAR-based representation of dynamic interactions among the social and natural components, GeoAgentKS was examined in a case study relevant to CWSs in Central Pennsylvania, USA. There are two specific objectives in this case study. The first is to explore a systematic set of methodologies for constructing the FOTAR framework using a knowledge-oriented strategy, including how to achieve knowledge acquisition, how to formalize knowledge representation, how to establish inter-geoagent communications, how to incorporate GeoAgents internal rules with concept maps and data, and how to validate the performance of the GeoAgents. Second, because GIS approaches have become more and more important in mitigating and managing natural or human-induced hazards (Oosterom et al. 2005; Radke et al. 2000), another objective of this case study is to illustrate the usefulness of the FOTAR representation for the practical and timely application of crisis and hazard management, especially in knowledge sharing and facilitating decision-making. In recent yeas, researchers have explored the use of geographic information technologies for hazard management in many different ways, such as evaluating hazard vulnerability (Cutter et al. 2000), enhancing human capabilities of quick responses to uncertain environments (Kwan and Lee 2005), providing more natural GIS interfaces for crisis management (Brewer 2005), improving geocollaboratory capabilities (Cai et al. 2006; MacEachren et al. 2005), and assessing regional risks (Bernknopf et al. 2006), etc. These research efforts, however, have paid little attention to represent multiple levels of qualitative social rules, policies, and emergency plans in GISystems to increase the users abilities in decision-making, institutional interactions, and knowledge sharing. This case study is intended to demonstrate that the GeoAgentKS can be effectively applied to address these issues. 21

23 5.1 The knowledge-oriented strategy of the case study To construct the FOTAR representation in the case study, a knowledge-orientedrepresentation strategy was adopted. Rather than first asking what data are needed, the case study started with domain knowledge acquisition. Knowledge about CWS in Central Pennsylvania was formalized as a combination of concept maps, mathematical models, and GeoAgents behavioral rules. The relevant geographic data was subsequently collected to support the knowledge representation. The knowledge acquisition methodology used was knowledge engineering: encoding external knowledge into a knowledge system via knowledge gathering, formalization, verification, modification, and reuse (Brule and Blount 1989; McGraw and Harbison-Briggs 1989; Chorafas 1990; Schreiber, et al. 2000). The knowledge sources used in the current research include both interviews with experts and interpretation of documents. The experiments using GeoAgentKS to gather domain knowledge in interviews with experts to elicit and represent their understanding of human-environment relations evolved in the CWSs can be seen in (Yu 2005). Examples are also demonstrated in Yu (2005) to illustrate how to integrate mathematical drought models (i.e., scientific understanding of the drought mechanisms), GeoAgentbased representation of social mechanisms, and a set of time serial drought data for representing a dynamic drought management process. In the current paper, the discussion is focused on exploring a set of knowledge-oriented methodologies for establishing the FOTAR-based geographic representation within GeoAgentKS via interpretation of documents. These methodologies include (1) knowledge-engineering techniques to derive knowledge from documented laws, regulations, and plans for constructing GeoAgents' behavioral rules and concept maps; (2) inter-geoagent communication mechanisms, (3) integration of knowledge representations with geographic data to achieve the GeoAgent-based representation of human-environment interactions, and (4) evaluation of GeoAgents behaviors under different environmental conditions. 22

24 5.2 Establishing concept maps and GeoAgents' behavioral rules To construct the concept map and the GeoAgents in GeoAgentKS, the knowledge sources used for formalizing GeoAgents' behavioral rules include a variety of documents. For local CWSs, these documents include emergency preparedness plans (e.g., power outage, contamination, flooding, etc.) and standalone drought contingency plans (e.g., College Township Water Authority 2002b and 2002a; Millheim Borough Water System 2003). On the state level, the documents used include the DEP's drought management plan (DEP, PA 2001), and the law of PA Code 35, Chapter 119 (i.e., "Prohibition of Nonessential Water Uses in a Commonwealth Drought Emergency Area", see Pennsylvania Emergency Management Agency and Council 1985). This research explored three major steps to encode the above documents into the GeoAgents' knowledge bases, including (1) sentence analysis, (2) pseudo coding, and (3) rule formalization. These steps are discussed below using an example of the "Power Outage Emergency Plan" documented within the Emergency Response Plan of the College Township Water Authority, which was collected from the office of this CWS in Sentence analysis for planning GeoAgents' goals, tasks and actions For sentence analysis, the first step is to plan GeoAgents goals, tasks, and actions. Achieving a goal state usually requires a set of tasks. A task consists of a set of actions (Ferber 1999). For example, "a book published" is a goal state, "to write a book" is a task, and "writing" is an action. A goal of this power-outage plan is derived from the overall text. For high-level tasks, the text is divided into multiple groups of sentences using a text editor (e.g., Word or Notepad). A particular task may be derived from a group of sentences. For low-level actions, the general method is to highlight the verbs of individual sentences. In the Power Outage Emergency Plan (Figure 4), if a power outage happens, it simultaneously results in two states: out of power, and water supply stopped. The primary goal for the water operator is to restore the water supply, although restoring 23

25 power can be a means to recover the water supply. Therefore, the goal state in this emergency plan is "water supply restored". To reach this goal state, the tasks and actions are identified from sentence analysis by grouping sentences and underlining key words. For example, Figure 4 shows that three high-level tasks are derived from grouping the entire text into three sets of sentences. The low-level actions are derived from the bold verbs. Figure 4: Sentence analysis of the Power Outage Plan of College Township CWS Name of the CWS: College Township CWS Emergency: Power outage Corrective actions: [Sentence group 1: checking the causes of the power outage] The station is considerably dependent upon electric power. The operator should visually inspect the station for smoke, fire, or alarms causing or resulting from power failure. The operator should determine whether the entire station or only portions of the station are without power. The operator should determine if the stations or the surrounding areas are without power. [Sentence group 2: determining how long it will last] The operator should contact the local Power Company or an electrical contractor. If the power failure is the result of the local power company, the operator should call the power company and inform the power company that a part or the whole public water system has been affected, and find out how long the power outage is expected to last. [Sentence group 3: taking actions to recover water supply] If the power outage is short term (less than two hours), the existing storage facilities should be able to supply the water system. If the power outage is expected to last more than two hours, the following procedures should be followed: 1. Emergency generator: obtain, connect, and use emergency generator to operate the Station A. If Emergency generator is not an option then, 2. Emergency interconnection: operate to the extent possible 3. Restrictive water use: contact the local fire departments, local radio station, and newspaper. 4. If the system pressure drops below 20 PSI (pounds per square inch), additional boil water order restriction must be issued and notification to the public. 5. Water hauling 24

26 Figure 5: Planning the GeoAgent's goal-driven actions from the Power-Outage Emergency Plan of the College Township CWS Task 1: To identify the causes of the power outage Check surroundings Check the Stations No power: assert power company failure Power: assert station failure Check fire, smoke, alarm Check failure: entire or partial Task 2: Station failure Partial & no fire: < 2 hours Otherwise: > 2 hours Goal state: Water Supply Recovered To identify low long it will last Task 3: To recover water supply Power comp. failure Use the stored water Use generator Use interconnection Inform the power failure Ask how long it will last Recovered < 2h, goal reached Check availability Available: goal reached Not available >> interconnection Check extent Operate: goal reached Restrict water use Contact fire Dep. & media Water hauling Goal reached Check water pressure PSI > 20: do nothing PSI < 20: contact media Goal High-level-tasks Low-level-tasks Actions Figure 5 shows the result of the sentence-analysis as a hierarchical plan of the goal, tasks, and actions. The three high-level tasks include (1) identifying the causes of the power outage, (2) determining how long the power outage will last, and (3) taking actions to recover the water supply. Each of the high-level tasks can be further decomposed into multiple lower-level tasks, and ultimately into executable actions. For instance, the task of identifying the causes of the power outage includes two lower-level tasks: checking surroundings, and checking the stations. If the surrounding areas are 25

27 experiencing a power outage, the GeoAgent needs to send a message to the power company. Otherwise, it needs to check the station. In order to encode these actions as behavioral rules, the key words appearing in the text are highlighted according to the types. For example, "whether" and "if" in Figure 4 are enclosed in boxes to denote the condition-indicator parts of the GeoAgent's rules. The verbs, such as "inspect", "determine" or "find out", "contact", "call" or "inform" are in bold to denote the action parts of the rules. The nouns are underlined to denote the key social and natural components involved in the emergency plan, and are also mapped into the concept map as concept nodes (see Figure 6) to allow the user to build and visualize the relations component in the FOTAR framework. Some of these nodes may designate GeoAgents (e.g., the fire department in the sharp rectangle nodes). Some others (e.g., power, stations, surrounding areas, system pressure, and emergency generator ) denote related environmental conditions (i.e., in round-corner rectangle concept nodes). This graphical visualization of the concept map allows the users to easily see what elements are related to this CWS. The critical states of the environmental elements are in italic in Figure 4 to denote the possible values (i.e., FACTS to fire the rules in the expert system) in given conditions. As shown in Figure 6, these potential states can be specified within the concept nodes. For instance system_pressure can be below_20_psi or above_20_psi ; surroundings can be no_power or normal ; and emergency_generators can be available or not_available. The oval concept node Power Outage is considered to be an event. To make the concept nodes understandable to the GeoAgent, the terms used in the concept nodes have to be the same as what are used in the GeoAgent s internal rules. 26

28 Figure 6:A concept map derived from text documents to establish the GeoAgent s environmental conditions Pseudo coding After sentence analysis, the knowledge engineer (or programmer) should have a clear understanding of the GeoAgent's goals, tasks, and actions at the conceptual level. Nevertheless, it is challenging, particularly for a beginner, to program a good set of JESS rules directly from a large amount of documents because both the rule structure and the JESS syntax have to be considered when encoding the executable rules. Constructing the rule structures in this case study therefore began by emphasizing the logical rule structure first by pseudo coding, and subsequently translating the pseudo coded structures into the detailed JESS syntax as a separate step. The pseudo codes can begin as brief outline expressed as natural-language phrases, as below, or as a list of key words in a text editor, and then refined in an iterative process. The programmer can list the required rules for accomplishing a task, the key conditions in the "IF" part of a rule, or the actions in the "THEN" part. 27