In the following, the developed models representing knowledge are explained. In contrast to previous work (see [Ahl07]), here, also the representation of perceptual knowledge besides the representation of action logic is considered. Furthermore, the hierarchization of these models is taken into account in order to reduce the complexity by focusing only the relevant parts of the real world.
Action model and action spaces
The actions of the ego system and the dynamics of its environment are represented in action models containing operator nets for each action (or sequence of actions as de- tailed below) of the ego system and each observed action of other independent agents. The action model can be generalized and specialized and it is a suitable description for long-term representation of action logic. However, due to the fact that the action model stores the conditions and effects of all actions, the calculation time for planning increases with the amount of stored information. Furthermore, the Petri Net representation is not suitable to derive a goal-directed sequence of actions directly. Hence, the focusing to a relevant part with a suitable format is also necessary.
In this thesis, action spaces are used to represent selected parts of the system’s long- term knowledge. In order to generate an action space, the effects of all possible operator nets based on the current situation are calculated and stored as experiences. Then, the resulting situations are used as initial situations for the calculation until already explored situations result or if a certain number of experiences is reached. Hence, the action space may also change during runtime according to the current situation. This action space results from the combination of long-term memory (action model) and short-term mem- ory (current situation) and can be utilized as working memory.
If an action model contains operator nets with uncertainty, also the resulting action space may be influenced. In such cases, operators are connected multiple times to the same initial situations and lead to different final situations (see Fig. 4.9). Hence, the
Chapter 4: SOM-based design of Cognitive Technical Systems 66
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initFigure 4.9.: Action space with alternatives
representation offers several action alternatives although only one actually exists. The resulting action space’s operators are weighted with the occurrence probability leading to a representation that corresponds to a Markov chain (see [CN10]). Although the percentage weighting could be used for planning (see [Ahl07]), here, this stochastic re- presentation is merely an indicator that the currently available knowledge is too general.
Hierarchical representation of action logic
In order to reduce the complexity of a real world’s representation, a hierarchical struc- ture is necessary. According to the proposed approach, human interaction can be divided into several action spaces differing with respect to the characteristics which have to be considered and the actions which can be performed. Hence, the result of human action- oriented reasoning is a hierarchical plan. The different steps of the plan are not detailed from the very beginning. They are detailed and modified dynamically if the human tries to reach the next sub goal.
A hierarchical representation with different degrees of abstraction can be realized by a structure of action spaces and meta action spaces with different orders. Here, the oper- ators of meta action spaces are related to other action spaces or meta action spaces (see Section 3.1.3). However, in order to utilize the flexibility of this representation, which is
Action models Meta operators Degree of abstr action Action spaces Initial situations Certain (successful) paths Action space generation
Figure 4.10.: Relations among action spaces, action models, and meta operators
restructured if the current situation and/or knowledge change, each action space has to be based on a generalizable action model. Thus, real world’s action logic is represented by several action models with different degrees of abstraction. Each action model can be extended and refined by learning and it can be used to generate a context-sensitive action space with a corresponding degree of abstraction.
In Fig. 4.10, the relations among action spaces, action models, and meta operators are shown. The action space on the lower part of the figure describes a certain part of the possible real world’s interaction and depends on the current situation and the criterion used to abstract the upper action space (e.g., the current goal). The operators of the lower action space are related to the basic actions of the considered systems and the lower action space itself represents a whole operator of the meta action space on the next higher level of abstraction. Furthermore, the operators of this meta action space may also be represented by meta operators. The assumptions and functions of each action space’s operators are represented by the corresponding action models on the right side of the figure.
Meta operators
According to [S¨of01c], a meta operator corresponds to a sequence of connected operators and is itself an operator on a higher hierarchical level. Its assumptions and function re- sult from the combination of the connected operators’ assumptions and functions. Thus, meta operators can be used to model actions on a higher degree of abstraction and de- scribe strategies or rules to reach certain goal situations.
Chapter 4: SOM-based design of Cognitive Technical Systems 68 o1 o2 o0 sdes ~ ~ ~ sdes-1 sdes-2 sdes-3
Figure 4.11.: Graphical representation of a meta operator
In this thesis, the real world’s action-logic is represented by a hierarchical structure of action models and meta action models with different degrees of abstraction (see above). Here, the meta action models describe the general assumptions and functions of certain meta operators with the same degree of abstraction. If a plan is to be generated, the meta action model on the highest level is used to generate a meta action space. The contained operators are represented by a separate action model, which again is used to generate action spaces with a lower degree of abstraction. However, the generation of these action spaces stops if the situation resulting from the corresponding operator on the higher level is reached. The whole procedure repeats until a generated action space contains basic operators, which can be directly executed by a connected technical system. However, besides the general representation of meta operators by the meta model’s operator nets, the special effects of meta operators can be described by a sequence of experiences containing operators of the next lower level of abstraction (see Fig. 4.11). Hence, these sequences are rules, which describe how to achieve a certain final situation from a certain initial situation. The representation within the proposed framework is re- alized by a class metaOperator (see Fig. 4.12) storing the name of the meta operator and an array of the class Experience. The name of the meta operators is used to relate the class metaOperator to the operators of a meta action model. The class metaOperator is used to store executed sequences of actions which transferred the system to a goal and subgoal respectively (see also Section 4.3.3).
The representation of meta operators by sequences of experiences can be useful in different ways. First of all, a plan to be executed can be extracted from existing meta operators if they contain the current situation and goal. However, this is only applicable if the situations of the contained experiences consist exclusively of characteristics with nominal parameters. Independently, from the contained situations, a meta operator can also be used to support the planning process. If several different paths to the goal are identified in an action space which is not completely correct (may happen if the operator’s assumptions were generalized), the meta operator provides the information whether one of the available sequences of operators in the path were already executed successively before. Hence, the system can prefer the previously executed path if the exploration of
metaOperator
name: String metaOperators:
ArrayList<ArrayList<Experience>> ...
Figure 4.12.: Class metaOperator
new (risky, but maybe faster) paths is not favored. Finally, meta operators can also be applied to speed up the generation of large action spaces. Here, the exploration of those paths is preferred which are also contained in meta operators leading to the current goal. If these paths do not exist, each path is considered equally. Hence, the action space generation alternates between an exploration based on breadth-first-search and depth-first-search.
Perception model
Some of the human’s most powerful characteristics are the capabilities to recognize per- ceptual patterns and to switch the focus of attention to relevant aspects. Through these capabilities, humans consider the perceived world in an abstract manner depending on the current situation and context and they are able to handle the complexity of the real world. Hence, the implemented perception model is subdivided into a recognition and an attention model. The recognition model contains the relations which are used to de- termine the parameters of the derived characteristics and the attention model contains rules representing which characteristics have to be focused in which situation.
The recognition model contains operator nets from the same quality as the ones in the action model. These operator nets represent the relations and are linked to the situations by their assumptions. Due to the fact that the relations can not be measured directly, new operator nets can only be added through the designer in advance (human interprets structure of the situation) or through learning from experiences (application of pattern recognition methods). The operator nets stored in the recognition model are used during perception in order to determine the parameters of certain derived characteristics from the parameters of measured characteristics and/or other derived characteristics.
The attention model is realized by a set of rules describing which measured and/or derived characteristics are to be contained in the systems focused situation. This may depend on the next operator to be applied and/or a set of characteristics (e.g., related to the current goal or to a certain initial situation of the defined operator). Thus, the
Chapter 4: SOM-based design of Cognitive Technical Systems 70 Goal focusedCharacteristics: ArrayList<Characteristic> priority: Integer actAssumptions: ArrayList<Situation> deactAssumptions: ArrayList<Situation> activationTime: java.util.Date ...
Figure 4.13.: Class Goal
focused situation can be set dynamically during perception and is a context-sensitive representation of the current scene and its mental mapping respectively. As the recogni- tion model, also the attention model can be set by the system designer and also refined automatically from interaction with the environment.
The learning of new relations and new characteristics to be considered is one of the main contribution presented in this thesis. Thus, the situation’s internal structure can be adapted to a certain environment automatically in order to realize autonomous systems performing independently from their initial knowledge. The related learning mechanisms are detailed in Section 4.3.5.