Output elements Knowledge management

This section presents the theoretical principles underpinning the development of situational awareness technologies. This includes the theoretical background of situation awareness with its situational calculus.

3.3.1 The Theory of Situation Awareness (SA)

Hidden situations often evolve from emergent patterns embedded in the real-world environment on an ongoing basis. In order to model the environment and its emergent situations, and evolving temporal patterns, a current situation model must capture explanations from real-world events in such a way that it facilitates understanding in SA.

The most established theory of SA is described in Endsley’s model [16], which describes the current situation model in a mental model at three hierarchical levels. As shown in Figure 3.2, Levels 1, 2 and 3 of SA correspond to perception, comprehension and projection respectively. The three components are the perception of elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future.

Perception is the most fundamental level in the current situation model, which involves the monitoring of the environment. Comprehension involves the understanding of how information perceived is integrated to create knowledge. Projection of events in the future, based on perception and comprehension, is the highest level of the current situation model. This is the anticipation of likely future events. Thus, SA enables us to recognize what pattern is developing in our domain of interest in order to figure out what to do next. This is a decision-making process that necessitates the mental model described in Figure 3.3.

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Figure 3.3: Endsley’s Mental Model [16]

As shown in Figure 3.3, the major sources of information for SA as a mental model are from system and interface knowledge and may probably be affected with direct observation from the real world and other factors [16]. The e3 is the active SA component obtained from the integration of system and interface knowledge. It is the crucial aspect of SA that is often prone to errors because its performance is dependent on the methodology used by the SA engineer as presented in [16]. Direct observation from real world, e4, and distant observer, e5, are other sources of information that may probably affect output of SA and consequently have an impact on decision-making. In some techniques, decision-makers may directly view SA results as being interactive or act over a network. However, it is understood from [16] that as a

Figure 3.2: A Hierarchy From The Endsley SA Model [16]

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state of knowledge, the patterns or results of SA are expected to have the capability of assisting with answers to the following four ‘W’ questions about any situation: (1) What is happening? (2) Why is it happening? (3) What will happen next? (4) What can I do about it?

Logic formalism or calculus is commonly used to express or elaborate on situational awareness as described in the next subsection.

3.3.2 The Phenomenon of Situation Calculus

Situation Calculus is a logic formalism, which expresses the inner awareness of a decision-maker (e.g.

managers, soccer players, robots, agents, etc). Situations are states of knowledge resulting from executing actions [8]. This idea is illustrated in Figure 3.4.

It is obvious in Figure 3.4 that the situations vary depending on the actions executed at any time step in the domain of interest. Specific example is environmental factors (e.g. climate change or weather) which significantly affect sales of products over a period of months. In new DBN technologies, BNs are dynamically evolved over time steps as temporal models of the situations. However, we logically derived mathematical equation (3.3) from Russell’s description of situation calculus [8] and express it as follows:

S0 = φ (δk, S0) S1 = φ (δk+1, S0)

S₂ = φ (δk+2, φ (δk+1, S₀)), By conjecture,

Si = φ (δk+i, Si-1) (3.3) for all values of k = 0, i = 0,1,2,…

Action-1

Action-2

…….…

……..

……..

Situation 0

Situation 1

Situation 2

Figure 3.4: Situations Resulted From Execution of Actions

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where δk is an empty action and the rest of the actions are not empty. φ is a function and predicate, which may change as a situation changes with time. The descriptions of actions used in logic reasoning are preconditioned and are explicitly represented as axioms. These are collectively integrated as knowledge.

Examples from Russell include Possibility (Poss ()) and Effect axioms. The situation calculus of the axioms is described and applied to an agent example that moves gold between two locations as follows:

the possibility axioms of a world state that an agent can go between adjacent locations, grab a piece of gold in the current location, and release some gold that it is holding [8]:

At(Agent, x, s) Λ Adjacent (x, y) ⇒ Poss(Go(x, y), s).

Gold(g) Λ At(Agent, x, s) Λ At(g, x, s) ⇒ Poss(Grab(g), s).

Holding (g, s) ⇒ Poss(Release(g),s).

The effect axioms state that, going from x to y results in being at y, grabbing the gold results in holding the gold, and releasing the gold results in not holding it [8]:

Poss(Go(x, y), s) ⇒ At(Agent, y, Result(Go(x, y), s)).

Poss(Grab(g), s) ⇒ Holding(g, Result(Grab(g), s)).

Poss(Release(g),s) ⇒ ¬Holding(g, Result(Release(g), s)).

It may become laborious to think of all possible axioms in real-world situations. In [8], it is added that this logic formalism becomes awkward when actions are numerous. This exemplifies a frame problem which can be addressed by self-awareness. This means that if a system is shown an action to be taken at a point of decision, the system will work only at that point. It implies that it is better to train a system on how to determine appropriate actions to be taken at any point in time so that it can work for ever. Thus, the approach of our DBN technology tends to minimize the worries of users for knowing the actions to be taken on some situations as these would be understandable when evolving the local dynamics from the global behaviour over time. The next section illustrates Bayesian networks as necessary and significant steps to modelling the DBNs.

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