The agent environment consists of the agent, action, percepts, events, beliefs, goals and plans, which is depicted as in figure 1 below. An agent is an autonomous entity. A goal is something the agent is working on or towards achieving it. An event is a significant occurrence that the agent should respond to in some way. A belief is some aspect of the agents' knowledge or information about the environment, itself or other agents. A plan is a way of realizing a goal. Thus the agent environment is composed of these basic components [₇].

The concepts of actions, percepts, events, goals, plans and beliefs are related to each other via the execution cycle that implements the decision-making of the agent. The execution cycle describes how instances of these concepts interact as an agent executes. Figure 2 below shows the agent execution cycle clearly.

Figure 1

Figure 1: Agent Environment

Figure 2

Figure 2: Agent Execution Cycle

The Execution Cycle consists of the following steps:

• Events are processed to update beliefs and generate immediate actions.

• Goals are updated

• Plans are selected from the plan library for achieving goals or handling events.

• A plan step is executed in the next plan, yielding new events, goals, belief, changes or actions.

The process for handling an event or attempting to achieve a goal consists of determining the relevant plans from the library of plans, determine the subset of the relevant plans that is applicable, select one of the applicable plans and execute the selected plan.

A goal-plan tree pictorially represents how the agent achieves the goal. The children of each goal are alternative ways of achieving that goal (OR) whereas the children of each plan are sub-goals that must all be achieved in order for the plan to succeed (AND). Let C be the number of plans that are applicable for each goal. Let S be the number of sub-goals per plan, except for the leaves of the goal-plan tree that do not have any sub goals. Finally let D be the depth of the tree, measured in terms of the number of goal levels. The number of ways by which the goal at the root of the goal-plan tree can be achieved is

C^{((SD-1)/(S-1))}

Figure 3 below shows a typical goal- plan tree. Let G be a goal that has C applicable plans in which each plan can be executed in p possible ways. Since each plan is an alternative, the number of ways that G can be achieved is the sum p + p + ... + p that is C * p. It is formalized as Δ_G(D) and Δ_G(1) = C

Δ_c(D+1) = C*Δ_p(D+1)

where Δ_p(D) is the number of ways in which the plan at the root of a tree of Depth D can be executed. For Δ_p(D+1) in which the plan has S sub-goals,

Δ_G(D+1) = Δ_G(D)^S Δ_(G)(D+1) = C * Δ_(P)(D+1) = C* Δ_G (D)^S

More generally, that for a goal-plan tree of Depth D the number of options is

C^{S(D-1) + ... + S(2) + S + 1}

This can be simplified further as follows:

Let

∑ = x^(n-1) + ... + x² + x + 1 x∑ = x⁽ⁿ⁾ + ... + x² + x ∑ = x⁽ⁿ⁾ - 1 ÷ x - 1 C^S(D-1) + ... + s² + s + 1 = C^{((S(D-1)/(S-1)))}

Figure 3

Figure 3: A typical goal plan tree of depth 3

Here a Distributed computing network consists of various agents is formulated. Each agent is configured for the plans, goals, actions and beliefs. Figure 5 below shows a typical DCN

Figure 5

Figure 5: Network of communicating and cooperating agents

The DCN as shown in figure 5 is formulated. Here the agents are capable of communicating and cooperating. The agents are dynamically created and hence the number of agents, their nature etc., are randomized[₁]. The algorithm for a Search Agent is as follows:

• Dynamically create as many agents as is required.

• Define the element to be searched. It can typically be any data. For example it can be file name, a text in a file or directory etc.,

• Create a random walk of the agent so that agents can search in parallel the various paths.

• Analyze the reliability of such a system using fuzzy logic which has a triangular membership functions.

• Typically the reliability is a probability distributed between 0 to 1 decomposed into various conditions like non availability, exceptions/errors and perfect scenario.

• Observe the results and record it and analyze it

Similarly the other tasks like remote desktop capture and remote updater are configured.