Application and Development of Artificial Intelligence Technology for the Data Management and Analysis in Forestry

Wei-ZhanGuo Liu-JinHao

School of Technology, Beijing Forestry University， Beijing, 100083, China

E-mail:Jackwzg007@hotmail.com

Wu-JiaDi

International Education School，Mudanjiang Normal University

Mudanjiang, 157012, China E-mail:Wujiadi48@hotmail.com

Abstract-Management of Chinese forests is becoming increasingly complex, involving a variety of data sets, software, and computing environments. To help with this task, we develop a system of experts for intelligent data management for forest and environmental monitoring. Once system has formed a solution to a problem, it uses the processing agents to execute each step in the solution. The heart of our system is a knowledge-based system called Palermo (Planning and Learning for Resource Management and Organization). In this article we’ll show how to integrate different problem-solving methods to automatically answer queries, thereby making some of the complexity of forest management.

Keywords-Artificial Intelligence; Data Management; Forest Engineering

I．CURRENT PRACTICE IN ANSWERING FORESTRY QUERIES

To see what Palermo must do, we’ll first look at how forestry queries are answered. To manage forests efficiently, decision makers need fast access to up-to-date information about the forests. These decision makers may frame queries to their staff to be answered. For example, a decision maker might ask: “what is the spatial distribution in the Greater Hinggan Mountains area?’The Greater Hinggan Mountains corresponds to six 1:20,000 maps (geographic information system files). There are GIS files covering topographic information, forest cover, soils, hydrology, geographic names, transportation, and so on. The user normally chooses which GIS files to begin with, and specifies the time for which this question is being answered. The GIS files might need processing to be transformed to a common map projection and datum, and to be joined to form a seamless GIS cover for the Greater Hinggan Mountains.

The topographic data needs to be transformed from irregularly distributed elevation points to a regular grid having the appropriate spatial resolution for the query. These processes are usually performed in a GIS such as ESNAR Chgres. The user needs to design the attribute database for the various GIS files and ensure the linkage between the graphical elements and the relational attributes. The GIS files will likely give the spatial distribution of Greater Hinggan Mountains for some older date. To know the forest cover’s current state, the user can

take satellite remote-sensing imagery integrate this imagery with a raster version of the topographic files, analyze the TM imagery for forest cover changes, and update the GIS forest cover files with these changes For example, a TM image mark out past clear cuts stored in a GIS forest-cover file Pink and recent clear cuts, which need to be identified and included in the forest-cover GIS file to provide the user with the forest’s current state.

To accomplish these tasks the user needs to run several processes in an image-analysis system .The updating of the GIS files is completed in a GIS It is important that the user receives visualizations of the remote-sensing imagery and the GIS files. These visualizations help the user understand the spatial analysis, and the accuracy assessment of each step. The visualizations are created as a result of processing .The system presents the answer to the query as a visual product and an updated GIS file. We view this process as a navigation through data and software repositories that is subject to time and cost constraints .This navigation is a knowledge-based problem solving exercise, in which a query’s solution comes from solutions to its parts For example, the above query would require expertise in forestry, topographic mapping, remote sensing image analysis, GIS operation, database design, and visual representations.

II．PALERMO: A PLANNING BASED APPROACH TO QUERY ANSWERING

Palermo handles this problem-solving exercise as a planning problem, which starts with a set of desired properties and tries to devise a plan-a set of steps-that produces a state with the desired properties. Palermo integrates three problem-solving methods: transformational analogy, derivational analogy, and goal regression.

Transformational analogy, also called case-based reasoning, solves problems by remembering, retrieving, and adapting complete solutions to similar problems. Because it relies strictly on retrieval and modification of past solutions, it does not perform any type of search.

Derivational analogy is a search-based, problem-solving approach that, rather than using complete solutions to previous problems, uses cases to choose among competing directions along search paths (that is, it is a knowledge-based research ) .

2009 International Conference on Artificial Intelligence and Computational Intelligence

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DOI 10.1109/AICI.2009.12

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Goal regression-for example, Strips6-is a search-based, problem-solving approach that eliminates sub-goal interactions by backtracking and reordering the sub-goals. Unlike derivational analogy, it does not use any knowledge to choose between competing directions along a search path.

We integrated these methods to improve the problem solver’s efficiency by remembering and reusing past solutions created by the problem solver. This strategy is similar to how explanation-based generalization speeds up the performance of concept recognition by operational lazing and generalizing concept definition.

A．Palermo’s structure The planner/controller is the core of our approach.

First, the planner/controller’s translator translates a user query into a conjunction of planner goals. The planner solves each conjunct separately and combines (merges) the solutions. After the planner constructs the plan, the controller assigns tasks to specific processing agents and coordinates their execution. The agents then execute the plan, which produces an answer to the query. Palermo relies on four different knowledge sources:

Meta-knowledge, which describes the information the system has: what data sets, in what formats, with what accuracies, and at what cost, are available to answer queries. If the meta-knowledge indicates that the answer to the query is available, the planner creates a plan that tells the processing agents where to retrieve the information, and where to deliver it.

A case base of plans whose execution by the agents can provide a result that may partially satisfy the query.

A case base of derivations. Derivations suggest what planning operator to choose at a given step in the planning, if a similar decision in the past resulted in the derivation of a successful plan.3 Manuela Veloso studied this approach during the development of the No Limit system.

Definitions of planning operators. After receiving the answer to a query, users can query the system further to refine the result or request a different presentation of the result. They can do this through elliptic references to a previous query to set the context for example. The last query requests a different presentation of the results [the user requests a map (GIS file) instead of a numeric answer], but the context is assumed to remain the same (the query is treated as an elliptic reference). We aim to create a reasoning system that forms new plans by using past experience, reusing of cases, and using operators that can lead to new, original plans.

B． The planner in detail The planner uses a generic “solve algorithm”. To avoiding searching a large state space, the solve-one-goal

procedure first uses transformational analogy to find a case that completely satisfies the goal. If such a case exists, the procedure applies modification rules to account for differences between the original case and the current goal. The system then tries to solve the other sub-goals and subsequently merge the solutions.

If the procedure cannot find a transformational case, it attempts to solve the goal by retrieving a derivational case. This approach has two purposes. First, where several solutions exist, a derivational case will justify selecting one solution over another. Second, derivational analogy lets the system select operators that would otherwise be overlooked, thus expanding the search’s scope by considering yet more solutions.

Finally, if a transformational or derivational case does not exist, the procedure uses goal regression.

Plan merging. Plan merging combines two or more plans into one plan while considering the interactions among sub-goals and between operators. Sub-goals are classified by their interaction.

Independent goals. Each sub-goal is completely independent of the other sub-goals It can be solved completely separately and, when merged, all of the operators in both plans are included in the final solution.

Dependent goals. Each sub-goal completely depends on the other sub-goals Achieving one goal satisfies other goals, so the problem solver can forego explicitly trying to solve every goal.

Resource-sharing goals. Each sub-goal can share resources with the other sub-goals.

Selecting a merging strategy is directly related to the type of goal interaction that the problem solver faces. Derivational analogy uses three merging strategies” to select the operators that will be considered during replay. Serial merging randomly selects a case and adds all the operators to the final solution before selecting a second case to merge. Round-robin merging replays by selecting an operator from each case at a time. Exploratory merging replays cases arbitrarily and chooses each step from a randomly selected case.

Our analogical plan-merging algorithm12 follows both serial and exploratory merging with a casual commitment strategy13 while considering operator interactions. APM uses serial merging for independent goals, and exploratory merging for resource-sharing and dependent goals. It applies techniques similar to those from the Network of Abstraction Hierarchies (NOAH) algorithm and the algorithm introduced by Qiang Yang.

The rationale behind APM is that redundant operators should be discarded. If the current operator from one plan is applicable and the current operator from the other plan is not, APM adds the applicable operator to the solution and proceeds with the merge.

Hopefully, as it adds operators, the state of the world

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will change so as to render the current inapplicable operator applicable. If both current operators are applicable, APM must select one to apply. If the application of one operator affects the second’s applicability, but the application of the second does not affect the first’s applicability, APM adds the second operator to the solution and proceeds with the merge. A problem occurs when the application of the first operator affects the second’s applicability and the application of the second affects the first’s applicability. This problem is called mutual precondition clobbering and is well known as the Sussman anomaly.

To address this problem, the algorithm chooses one operator and adds it to the solution, and the merge proceeds. The hope is that, after the algorithm adds operators to the solution, the clobbered preconditions will become true again. The planner in action. Let’s assume the system receives this query with two sub-goals: “Create a map that shows the forest depletion in Greater Hinggan Mountains over the past 10 years, and create a map separating western cedar and Douglas fir canopy over Greater Hinggan Mountains.” Our system will provide a map (GIS files) of the desired site that contains the forest distribution per age and species dated 10 years ago, TM and color infrared geo-coded imagery over the site, and image processing and visualization tools. To create the forest depletion map over the past 10 years, the TM imagery must be processed to detect the areas representing depleted forest cover. Color infrared imagery can be used to improve the boundaries.

Solving the first sub-goal. The planner first tries to produce a plan for the first part of the query conjunction-that is, computing the forest depletion. If the system does not have a case that can satisfy the query directly, but does have one that classifies imagery and another that can display a map, and has an operator that expresses forest depletion by classifying an image and displaying the result, then goal regression can resolve the problem. The operator’s preconditions replace the original goal, and the planner attempts to solve them separately. Because cases exist to solve each of the sub-goals, the planner retrieves them, adapts them to the current problem, and merges them into one solution. If the solutions for the two preconditions for the depletion-overlay operator are as shown in Figure 6, APM behaves as follows: (1) It sets the current operator from plan to start (visualization program), sets the current operator from plan to start (visualization- program), and sets the time index to zero (the time index indicates the point of insertion of an operator into the solution). (2) It determines if the current operators are applicable at the current time index. In the example, both operators are applicable. (3) It checks for operator redundancy-that is, are they identical operators, or is the sub-goal for which they are used already satisfied by the side effects of previous

operators in the solution? In the example, the current operators from both plans, start (visualization program), are identical, so APM removes one of them. If we assume that the deleted operator is from plan 2, then the current operator in plan 1 is still start(visua1ization-program) , the current operator from plan 2 is now set to load (map (Site, Time ) ) , and the solution is still empty. (4) The current operator in plan 2 is not applicable, but the current operator from plan 1 is, and it does not clobber the preconditions of plan 2’s current operator. Therefore, APM inserts the operator from plan 1 into the solution. The current operator from plan l is now set to load(tm-image (Site, Time) 1. For all of the remaining operators, there are no conflicts. APM selects one of the plans (currently this is a random selection) and appends it to the solution, and then appends the other plan.

The most important consideration, whenever an operator is inserted into the final solution, is to update the current state description. All search-based methods actually simulate the application of each operator for example, Strips’ use of add and delete lists. If a new operator is added, the state description at the point of insertion and all the following time indices must reflect the insertion’s effect.

III．CONCLUSION Our System not only answers queries, but also provides tools that let us apply AI methods to other forest and environmental applications We are exploring another system that verifies compliance with forest legislation (the Forest Practices Code) for large-scale logging in complex watersheds, involving many land values, such as biodiversity, wildlife habitat, and economic sustainability.

China has recently produced a set of guidelines for maintaining biodiversity, which specify the conditions that must be adhered to when harvesting forest l4 We believe that those guidelines can be represented as a set of rules (essentially, Horn clauses). We could then produce a tool that verifies whether a given five-year harvest plan for a forest is compatible with the guidelines.

This tool could, for instance, take the form of several GIS map levels representing the various restrictions and permissions in the guidelines and legislation. The system would then overlay these levels on forest cover maps and on timber supply area maps, thus highlighting any area in conflict with the guidelines and indicating the time steps at which these conflicts will arise.

By using this approach, the system would be able to help design other components of harvesting strategies. The system could then use a reasoning technique, such as backward chaining, to check whether a harvesting strategy meets the guidelines. This would involve representing the strategy as a set of goals, and backward-chaining the guideline rule set on these goals. A trace of reasoning would indicate, in the case of failure, which guidelines are violated and why.

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Acknowledge：The paper is supported by Key Projects in the National Science & Technology Pillar Program during the Eleventh Five-year Plan Period (2006BAD11A15).

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