0227-B4

An agent-based framework for selective management system

A.M Rasol 1

Abstract

The reason for focusing to agent technology is because of the ever-growing
complexity of forest information systems and the increasing difficulty to plan
in a dynamically changing environment. Forest planning and scheduling problems,
and database technology have for a long time realized to deal with the
robustness by developing suitable technology, Decision Tree Learning on very
large forestry data sets. In this paper an implementation model for agents with
layered architecture in a tropical forest Selective Management System based on
database infrastructure is proposed. The model overcomes the non-parallel in
implementing the various agent processing functions together with the
limitations imposed on concurrent execution of agent actions. An example is
provided to illustrate how the various agent functions are imposed on concurrent
execution of agent action using Conflict Resolution technique.

1.0 Introduction

One of the motivations for focusing to agent technology is the increasing
complexity of forest information systems and the increasing difficulty to
predict and plan for dynamically changing environments. None of the forestry
applications seems to use existing agent architectures and yield them as a
subject of research. Three-directions of agent architectures have been developed
over the past decades: reactive, deliberative, and coordinating architectures
(Muller and Pischer 1993). Some research efforts have been contributed to the
exploration of how hybridisation of these architectures as layered architectures
(Fergusson 1992; Firby 1992).

In forestry applications, the main issue is robustness in environment that is
prone to dynamically changing environments. On the other hand, is the issue that
database technology has focused on by developing the concept of information
theory (Shannon and Weaver, 1949). If one managed to combine databases and
agents technologies, there should be a much better chance of employing layered
agent architectures to forestry agent-based systems. These applications are
large-scale systems that must ensure a high degree of reliability while
maintaining high performance by executing concurrent decision trees. This paper
involves the implementation model for applying layered agent architecture in
forestry database environment. The implementation of the architecture is
realized by all level of components with concurrent execution of agent actions
and orthogonal implementations of agent functions in a decision trees (DT). In
DT, all the reliable rules for parallel execution are defined. Contingency
actions, in case of failure of some of the components of the plans, are also
defined within the DT. A Decision Tree Model (DTM) will be employed to represent
DT to ensure the degree of high concurrency in agent actions are increased
before the contingency action is committed to the database infrastructure.
Another advantage is that the addition of new functions to the agents does not
require detail knowledge of original functions due to the capturing of plans
together with their active state within the same DT.

The remainder of the paper is organized as follows. Section 2 presents the
proposed intelligent agent layered architecture, the role of each layers in the
aspects of agency, and describes the detail architecture components. Section 3
contains a case study in implementing a DTM typically to Selective Management
System based on database infrastructure. Section 4 and section 5, gives the
future works and conclusion respectively.

2.0 Intelligent Agent Architecture

Here we present an intelligent agent architecture in an attempt to answer
some of the shortcomings of existing forest information systems. Our
architecture consists of: reactive layer, deliberative layer, social layer,
knowledge base module, problem and goal module, and planning and scheduling
module, forestry database, sensors, coordinator and communication interface.

In the following sections we will look closely at these components with the
assistant of figure 2.1.

Figure 2.1: Agent Layered Architecture

3.0 Implementation Model

The agent's perceptions (sensors) are translated into reading the database
objects, while its actions are translated into performing write operations to
these objects. Reading and writing are done within normal set of rules having
atomic, consistent, and permanent properties (Ensor and Gabbe 1988). The agent's
Knowledge Base (KB) is stored in a database at each agent layer. User Interfaces
of the relevant information available in underlying layers are materialized. The
belief generator of each layer acts as an interpreter and problem solver for
automated reasoning uses its interface to update the agent's beliefs. These
beliefs are stored in the knowledge base database of the corresponding layer.

The PG part of the agent contains the Problem identification (P) and Goal
activation (G) modules. In light of the given goals at each layer, a goal is
activated based on a chosen domain problem stored at this layer. The higher the
layer is, the more complex are its plans and the more difficult it gets in
representing them. Plans are to be modeled as DTM as will be shown later in
Section 4.0. A goal can be activated only by the problem identification module
of the corresponding layer of the agent or another agent in the case of the
social layer.

In the context of the activation mechanisms the problem receives incompetence
messages from the Planning and Scheduling (PS) module of the underlying layer of
the architecture, whereas in the implementation model events that mainly
influence the problem identification module are belief updates. The particular
activation mechanism of the problem identification depends on the database
infrastructure. An active database infrastructure is recommendable, which
triggers the problem module with each new situation. Other kind of events to be
monitored by the problem module is events that are the result of the activated
goal on the same layer: the success/fail of the actions is returned from the PS
module. In the social layer, some events coming from other agents through the
communication interface. The PS module receives the success/fail results and
other coordination messages of the agents incorporated in jointplans in
which several agent collaborate in order to achieve a common goal (Lavesque et
al. 1990). In the PS part of the agent, activated goals are realized by means of
activities. In the next section, these activities are defined using a decision
tree model.

4.0 Decision Tree Model

Overall, the activities in the decision tree model are reflected by a number
of decision nodes. Every decision node member is committed to achieving some
goal while also believing the other decision node members are pursuing that
goal. The decision node members exchange messages to update their mutual beliefs
and synchronize their behaviour, establishing what is called joint commitment.
In general, a decision node member cannot commit unless all its decisions are
terminated. However, if one of the nodes fails, its parent nodes does not have
to abort. It has the choice between: ignoring the unessential condition (node),
reinstating the sub-node, activating another compensating sub-node , or
abort.

The sub-node may commit or abort independently and appear atomic to other
sub-node. Therefore, executing compensating node whose role is to mitigate the
permanent effects of the original sub-node must compensate the other sub-node.
By defining these sub-nodes, the goal of integrating the agent actions together
with their active state within a DT.

Given this decision tree model, the procedures are managed as follows. The
database infrastructure schedules the sub-nodes at each agent layer. The
scheduler is responsible for identifying conflicting nodes and serializing them
using locks specified in a Conflict Resolution (Martial 1992). Other
responsibilities of the scheduler include possible deadlock detection and
resolution. The generated schedule is then passed to the Execution level, which
performs the agent actions on the forestry knowledge database in an efficient
and reliable way.

At the lowest layer, the sub-nodes are simple DT framework. Operations
involved are database read/write. At higher layers, operations become more
abstract. This allows further exploitation of the semantics of the operations by
commuting their execution on the basis of the predefined Conflict
Resolution. The great advantage of this approach is the direct transformation of
conflicts between various agent behaviors to a conflict of decision nodes on
data, which is automatically handled by the database scheduler and require no
direct and explicit analysis on a combinatorial basis.

When coordinating autonomous agents in order to enable their Social layers to
satisfy their collaborative goals. That's why coordination at the social layer
of one agent cannot initiate a sub-node at another agent. Another reason is that
agents can be deployed in a heterogeneous environment, in which each agent
operates on a different database infrastructure. The role of the Problem
Identification and Goal Activation module is to coordinate between them using a
protocol similar to the 2Phase Commit protocol (Ensor and Gabbe 1988). It
uses the Communication Interface module, which is responsible for the exchange
of messages between agents in conversation languages such as KQML (Finin et al.
1994) and FIPA 97 (FIPA 1998).

5.0 Case Study

In this illustrative case study, it is required to build an agent that
determines the forest treatment types and status of forest stand in a selected
forest. The aim of this example is to demonstrate the ease of development of the
agent in various functions. By using DTM, the degree of concurrency is largely
increased; thus increasing the agent flexibility in executing its plans. The
overhead of coordinating these concurrent actions is undertaken by the
underlying database infrastructure. Moreover, due to the definition of the agent
plans in the form of coordination, the plan is self-contained and includes the
compensating actions, needed in case of failure of one or more the sub-nodes.
This allows existing conflicts to be implicitly solved, avoiding a later
explicit solution from the user.

The forest stand (S) has a current inventory data. The agent's plan is to
process the inventory data allowing the best solution strategy for the user and,
at the same time, recommending the user's needs. These needs include the
following: (a)interpretation of forest inventory data, (b)needs for selective
forest management system, and (c)collaborative forest planning operations

In this implementation, the fulfilling needs (a) will at first initiated and
then expanding the system for further needs. From the DT, the independence in
developing the various functions can be easily concluded. Forestry database
consists of four basic objects: the forest stand object (F), the tree number
(N), the tree diameter classes object (D), and tree species (S). A set of events
that occur is the category for D classes >45cm, +30 - 45cm, +15 - 30cm) and
the tree species types (Dipertocarps and Non-Dipterocarps).

Whereas the N and DBH are updated fairly regularly by interval tasks, the F
and S remains locked for a whole period. This non-shared lock is then released
for a certain period in which the new value to be locked is specified, and the
lock is renewed for another period. For an outline of all operations see Figure
5.1. To implement (a) only the agent Reactive Layer is needed. The agent has a
view of the Forest Resource (FR) i.e. forest inventory data having set of
variables such as: tree number, tree species, and tree diameter. Its problem
identification is simple: to analyse the FR using other software. Launching a
simple orthogonal task T1 to update the F inventory data activates the
corresponding goal, namely, a forest inventory analysis assignment. Another
task, also launched from the agent Reactive Layer, is the classification
assignments consisting the total number of trees per hectare, total volume per
hectare according to tree diameter classes and tree species types.

Figure 5.1: Domain Problem Definitions

This plan, T2, determines the data set required for the needs of practicing
selective management system of the Forest Stand. Problem (b) involve more
deliberation. The place to implement it is at the agent Reasoning Layer, where
the agent tries to fulfill more complicated goals. Usually, the user will
observe the required management options and must be coordinated with getting the
necessary variables from the existing FR. So, at this layer, the agent watches
the type of expertise to get involved. When a good deal is observed by the
situation recognition module, the goal "selection of management options" is
activated and a DT is executed by initiating the root problem T3 at the
deliberative layer to decide the necessary assignments. This root problem
creates three sub-problems at the lower level: T31, T32, and T33. T31 updates
the F (T31 is of the same type as T1). For example, T32 allocates the list of
silviculture treatments by triggering the number of trees according to their
conditions (i) diameter class >45cm, number of trees ≥ 25, number of
Dominance trees ≥25, number of vine free trees ≥25, (ii) diameter class +30 -
45cm, number of trees ≥32, number of Dominance trees ≥32, number of no-climber
trees ≥32, (iii) ) diameter class +15 - 30cm, number of trees ≥ 100, number of
Dominance trees ≥100, number of no-climber trees ≥100. T33 waits until the
release of the lock on the FR to make the final decision. Under the DT these
sub-problems (T31, T32, and T33) can run concurrently with each other or with
other problems. All the designer has to do is to define the appropriate behavior
in case of failure of these problems according to the rules mentioned in Section
4.0 (e.g defining compensation node). Then, it is up to the database
infrastructure to learn and execute these DT, resolve possible conflicts, and
handle exceptions resulting from the failure of one the sub-problems. Figure 5.2
shows a semantically concurrent execution of these DT, expressed as a partial
order between them. The graph proceeds from left to right and the edges mark the
order between conflicting pairs of sub-problems.

Figure 5.2 A Semantically Concurrent Execution

The agent Social Layer is needed for satisfying collaborative needs (c). In
this case, determining forest planning operations based on the forest stand
inventoried area. At this layer, the agent has a view of the Forest Stand, and
the rule set from the cutting regimes lists, silviculture treatments, and forest
stand status observed by the Problem Identification. Also depending on who is to
take the initiative, it observes activating messages from the other agent(s).
When the opportunity comes, DT, similar to T3, are instantiated at the Reasoning
Layer of both agents. Although, in principle, a single parent for both
sub-problems is sufficient (see Figure 5.3), this is not allowed due to the
autonomous nature of agents. A model having a parent (root) problem at the
Social Layer of each agent (see Figure 5.4) and having a coordinator
coordinating between these two root problems seems to be an appropriate
solution.

Figure 5.3: Single node (one root) for nonautonomous
agents

Figure 5.4: Loosely coupled node (two roots) for autonomous
agents

6.0 Future Works

The implementation model is a first step toward a full representation of
agents layered architectures in terms of activities based on DT. The aim is to
make use of the knowledge of decision semantics to increase degree of
concurrency. This DTM opens the way for another aspects in their use, namely the
representation of agent plans. By capturing the plans together with their active
state within the same structure, they form a powerful tool for detecting and
resolving conflicts in deliberative layer. However, further refinement and
fine-tuning has to be done to the DTM to better serve its new purpose as a
generic representation tool for the various planning strategies. The questions
on what kind of planning strategies can be represented as DT, or conversely, how
these can be adjusted to meet particular strategies.

7.0 Conclusion

In this paper, an attempt is made to complement agent technology with
forestry database technology to increase system robustness. The decision trees
model provides an independent implementation of agent operation. Representing
agent plans in terms of DTM opens the door for a large degree of concurrency
between agent actions, thus simplifying agent plans and at the same time without
having the developer worrying about coordinating them. Additionally, a clearly
defined DTM within a semantically concurrent execution provides a neat design
and avoids much of the undesired negative interactions between the various agent
actions.

References

Ensor, J.R., and J.D. Gabbe, 1988. Transactional blackboards. Readings in
distributed artificial intelligence. Eds. Bond, A.H and Gasser, L. Morgan
Kaufmann, California.

Fergusson, I.A., 1992. Touring Machines: An Architecture for Dynamic,
Rational, Mobile Agents. Ph.D Thesis, Computer Laboratory, University of
Cambridge, United Kingdom.

Firby, R.J., 1992. Building Symboliuc Primitives with Continous Control
Routines. In.J.Hendler, editor. Proceeding of the 1 st International
Conference Intelligence Planning Systems. Morgan Kaufmann.

Finin, T., R. Fritzon, D. Mckay, and R. McEntire, 1994. Draft specification
of the KQML AgentCommunication Language. The DARPA Knowledge Sharing
Initiative External Interfaces Working Group, Baltimore.

FIPA, 1998. Agent Communication Language. Foundation for Intelligent Physical
Agents, Geneva, 1998. http://www.fipa.org/.

Lavesque, H.J., P.R. Cohen, and J.H.T. Nunes, 1990. On acting together.
Proceedings of the 8 th National Conference on Artificial
Intelligence (AAAI-90), 94-99. Boston, MA:AAAI Press.

Martial, F. 1992. Coordinating plans of autonomous agents. Lecture notes in
computer science 610, Springer Berlin.

Muller, J.P., and Pischel, M., 1993. The agent architecture inteRRaP: Concept
and application. Research Report RR- 93-26, Deutsches Forschungszentrum fur
Kunstliche Intelligenz, Kaiserslautern, Germany.

Shannon, C.E., and W. Weaver, 1949. Mathematical Theory of Communication,
Urbana, IL:University of Illinois Press.

1 The author is a Research Officer with the Forest
Research Institute Malaysia and currently doing his PhD as a Research Student,
Centre of Computing Intelligence, De Monfort University, Leicester, United
Kingdom.

Centre of Computing Intelligent
De Monfort University, Leicester, LE 1
9BH, UK
44-01162 577939
email: [email-masked]