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8. Advancement and implementation of Toda's model

In the last years, the interest in Toda's theoretical approach has risen again. This can be seen, among other things, by the frequency with which he is quoted approvingly, for example by authors such as Frijda, Pfeifer, or Doerner.

The increased reception of Toda conincides with a renewed interest in the construction of real autonomous agents. In this respect there have been several approaches to modify Toda's model on the basis of recent findings from emotion psychology and implement it partly in a computer simulation or a robot. The works of Aubé, Wehrle, Pfeifer,  Dörner, and others are presented briefly in this chapter.

8.1. The modification of Toda's urges by Aubé

Michel Aubé pointed out the problems inherent in Toda's system of urges (Aubé, 1998). On the one hand he criticizes Toda's classification of urges : Thus grief for example is classified as one of the rule observance urges. On the other hand he points out that Toda classifies a number of urges as emotions which one would call rather a need (e.g. hunger). Finally he notes that some urges represent what Frijda  calls action tendencies and not the emotions themselves, for example rescue or demonstration .

Aubé therefore suggests first of all to give up the definition of urges as emotions but to understand them rather as motives. Aubé differentiates these motives in two classes: Needs such as hunger or thirst represent a motivational control structure, which make resources of the first order available  and their management possible. Emotions such as annoyance or pride are motivational control structures, which create, promote or protect resources of second order. Such control structures of second order are, for Aubé, social obligations ( commitments ).

Fig. 6: Two control layers for the management of resources (Aubé, 1998, p. 3)

Commitments are for Aubé the central factor with emotions:

"Since emotions in our model are specifically triggered as handlers or operators whenever some commitment is seriously at stake, we see commitments as the critical hidden variable behind any emotional reaction, just as the concept of force in physics is understood as the general cause to be invoked whenever a change in motion is observed."


(Aubé, 1998, p. 4)

Within autonomous agents, commitments represent dynamic units, active subsystems which look out after significant events which are of importance for their fulfilment or injury. They register as variables, for example, who is obligated to whom,  until when and about what).

""To whom" also means keeping a count of the levels of commitment one has with frequently encountered others. (...) "About what" typically refers to quantifiable first-order resources that the commitment insures access to, or to appropriate tasks for getting these resources. "Until when" means that a commitment is generally attached a precise schedule for its fulfillment."


(Aubé, 1998, p. 4)

Aubé has developed a general call matrix for fundamental classes of emotions combining the approaches of Weiner and Roseman. He arranges Toda's social urges in this matrix.

Fig. 7: Call structure for fundamental emotions (Aubé, 1998, p.4)

Fig. 8: Allocation of Todas urges to the call structure for fundamental emotions (Aubé, 1998, p. 5)

Aubé comes to the conclusion that his modified version of Toda's urges agrees with the most important theories of motivation and with his theory of emotions-as-commitment-operators. Such a control structure is for him a substantial condition, in order to design cooperative adaptive agents which can move independently in a complex social environment.

8.2. The partial implementation of Toda's theory by Wehrle

Wehrle converted the basic elements of Toda's social Fungus Eater into a concrete computer model (Wehrle, 1994). As a framework for this he used the Autonomous Agent Modeling Environment (AAME). The AAME was developed specifically to investigate psychological and cognitive theories of agents in concrete environments. To AAME belongs an object-oriented simulation language with which complex micro worlds and autonomous systems can be modelled. Moreover, a number of interactive simulation instruments belong to the system with which the inspection and manipulation of all objects of the system are possible during execution.

For the concrete implementation of social Fungus Eaters some additional assumptions were necessary which cannot be found in this way in Toda's work: They keep a certain distance to each other, in order to avoid conflicts around food finds or ineffective ore collecting. On the other hand they keep in loose contact, in order to be able to help one another in an emergency.

In place of pre-programmed urges Wehrle's model uses a cybernetic control loop in which the energy balance of an agent is linked with hedonistic elements.

The complete model of the social Fungus Eaters looks as follows:

Fig. 9: Model of a social Fungus Eater (Wehrle, 1994)

The emergent behaviour of the agents in his model is described by Wehrle as follows:

Agents can mostly be found at sources of food or places of ore discovery.

At the food sources there is a gradual change of the composition of agents.

Agents with similar hunger value form groups of 2 to 5 members.

The groups dissolve, if the agents are filled up to a certain energy level again or if other agents come to the food source.

Especially hungry agents push less hungry agents aside and show other kinds of antisocial behavior.

Surely this implementation is only a small first step to actually develop an autonomous system based on Toda's principles. It shows however that it is in principle possible and that already in a very restrictive implementation first emergent effects show themselves.

8.3. Pfeifer's "Fungus Eater principle"

Pfeifer has described the construction of autonomous agents after the Fungus Eater principle, based on Toda's model.  (Pfeifer, 1994, 1996).

His starting point was the only partial success of his model FEELER as well as other attempts of the "classical" AI to develop computer models for emotions. He formulates his criticism in six points:

  1. The assumption that emotions are isolable phenomena: On the one hand this provides a set of sources of error, because there is no generally accepted definition of "emotion". On the other hand, "emotional behaviour" is programmed into the system using rules; emergent development of emotions is thus not possible. Much points however to the fact that emotions are emergent phenomena which can not be separated from the overall system of an agent.
  2. Frame-of-reference: The models typically used in AI are of intentional nature, i.e., they work with goals, knowledge, beliefs. These models tell us nothing about the mechanisms underlying an emotion since they are rationalizations developed post hoc. Thus they are attributions coming from the observer and no images of the mechanisms of emotion.
  3. Situatedness: The knowledge how an agent has to react in a certain life situation is not stored once and for all, but is generated anew again and again in such situations. In an uncertain, fast changing and unpredictable environment it is not possible to store solutions for all problems from the outset in the system.
  4. Embodiment: Most AI models work exclusively with a software simulation. Real-life agents have, however, a body to move freely in their world. The ability to be able to interact through a body with the world generates completely new learning and problem solution effects, which are not derivable from a pure software modelling.
  5. Limited tasks: AI models design their agents for a narrowly defined task. (With FEELER this was an emotion-eliciting situation in an airplane.) This does not have anything to do with the real world in which an agent always has to execute several tasks, often from different problem areas. A complete autonomous system thus needs devices in order to be able to interact with the real world and mechanisms which make it possible for it to act really autonomously.
  6. Overdesign: Apparent complexity in the observable behavior does not mean inevitably an identical complexity in the underlying design. That was already demonstrated by Braitenberg with his Vehicles (Braitenberg, 1990). Most AI models tend to implement complex instead of simple mechanisms because they choose a top-down approach which proceeds from hypotheses over a mechanism and does not let the agent develop such mechanisms itself.

Pfeifer's Fungus Eater principle assumes that intelligence and emotions are characteristics of "complete autonomous systems". Therefore he concerns himself with the development of such systems. This way, one can also avoid to lead a fruitless debate about emotions and their function:

"Since emotions are emergent, they are not engineered into the system, which implies that there can be no set of basic emotions out of which all the others are composed. Identifying the basic components would also imply the existence of clearly delineable functional components which, given that emotions are emergent, is not a sensible assumption.


Another example concerns the function of emotion. If there is no emotion component we cannot sensibly be talking about its function. What we can say is that the way the complete system is organized enables it to display certain adaptive behaviors. And a convenient way of describing this behavior and communicating about it is in terms of emotion."


(Pfeifer, 1994, p. 16)

The Fungus Eater principle means at the same time that one must observe an accordingly designed agent over a longer period of time in order to see which behaviour develops under which conditions.

Building on these remarks, Pfeifer developed two models of an autonomous Fungus Eater:  a Learning Fungus Eater with a physical implementation as well as a Self-sufficient Fungus Eater as a pure software simulation. 

The Learning Fungus Eater is a small robot, equipped with three kinds of sensors:  proximity sensors determine the distance to an obstacle (high activation with proximity, low with distance);  collision detectors  are activated with collisions;  target sensors can detect a goal, if they are within a certain radius around this goal.  The robot has two wheels which are propelled independently by two motors. 

The Learning Fungus Eater has two reflexes:  collision-reverse-turn and if-target-detected-turn-towards-center-of-target.  The control architecture consists of a neural net which can be changed partially by Hebbian learning:

Fig. 10: Control architecture of a Learning Fungus Eater (Pfeifer, 1994, p. 10)


The entire control system consists of four layers:  the collision layer, the proximity layer, the target layer, and the engine layer.  The only task of the robot is to move around.  Its environment looks as follows:

Fig. 11: Environment of the Learning Fungus Eater (Pfeifer, 1994, p.10)

What happens now if the robot is activated?  First it will hit obstacles.  Each reverse-and-turn activity makes possible Hebbian learning between the proximity layer and the collision layer, until the robot has learned to evade obstacles.  It looks from the outside as if the robot can anticipate obstacles.  Pfeifer points out that usually for such an "anticipating" behaviour an architecture with several layers is suggested, while one layer is actually sufficient.  Pfeifer explains two further phenomena of the robot:

"What also happens if the "Fungus Eater" moves about is that if there are target sources (light) along walls, it will start following walls, even in the absence of light. We can characterize the behavior adopting the intentional and the "emotional stance". People who observed the robot's behavior have made remarks like the following (sic!): "Oh, it's following walls because it knows that food is normally along walls, so it's a good strategy to follow walls." "It hopes to find food because it has learned that food is normally found along walls." If the "Fungus Eater" is dragged into a corner with a light source where it can no longer move it will wiggle back and forth in the corner for some time and then turn around and take off. People have said that it was frustrated or annoyed and turned away."


(Pfeifer, 1994, p. 11)

This demonstrates clearly, according to Pfeifer, that a system without strategies, without anticipation mechanism, without knowledge over sources of food can show a behaviour which is classified by observers as purposeful and motivated - but which results, however, only from the interaction of the system with its environment. 

The Learning Fungus Eater is in as much no complete autonomous system as it cannot support itself.  This is why Pfeifer developed the Self-sufficient Fungus Eater, at first only as a software simulation.

The agent is in this case is situated in a "Toda landscape" with fungi as food and ore for exploitation.  The action selection here is clearly more complicated:  the agent can explore (look for ore or food), it can eat or collect ore.  What it does is determined by the central variables "energy level" and "collected ore quantity".  For the action selection in each given situation the agent has only one rule:  "If the agent is exploring and energy level is higher than amount of ore collected per unit time, it should ignore fungus (but should not ignore ore), if not it should ignore ore (but should not ignore fungus)." (Pfeifer, 1994, p. 12)

Here also, according to Pfeifer, the result for observers is a state to which they attribute a high emotional intensity

"If they see, e.g. energy level going down....and they see the agent moving toward a patch of fungus....they really get excited about whether it will make it or not. Such situations are normally associated by observers with emotion: there is time pressure for the agent which may be associated with fear or at least with an increasing level of agitation (This is a typical consequence of self-sufficiency). However, we know that all there is in terms of mechanism within the agent is the simple rule. Thus, if we want to talk about emotion at all, it is emergent....In spite of its simplicity the "Self-sufficient Fungus Eater" shows in some situations behavior that we might associate with high emotional intensity."


(Pfeifer, 1994, p. 13)


Pfeifer cautions in the same essay that these few findings can naturally not explain what emotions really are.  However, he expects a lot from the further pursuit of this approach, even if it is very time intensive; more, in any case, than from computer models which deal with an isolated emotion model.


8.4. The approach of Dörner et al.

Dörner has developed a computer model that integrates cognitive, motivational, and emotional processes (Dörner et al., 1997; Dörner and Schaub, 1998; Schaub, 1995, 1996): the PSI model of intention regulation. Within the framework of PSI he developed the model "EmoRegul" which is concerned particularly with emotional processes.

PSI is part of a theoretical approach which Dörner calls "synthetic psychology".  This approach tries to analyze, by designing psychological processes, how these processes can be represented as processes of information processing.  Dörner's starting point is similarly to Toda's if he writes, "..that in psychology one may not divide unpunished the different psychological processes into their components "(Dörner and Schaub, 1998, p.1). 

Core of the PSI model is the concept of "intention".  Schaub defines intention as an internal psychological process,


"... defined as an ephemere structure consisting of an indicator for a state of lack (hunger, thirst etc..)  and processes for the removal or avoidance of this state (either ways to the consummational final action or ways to avoidance, in the broadest sense "escape")." 

(Schaub, 1995, p. 1)


The PSI agents are conceived as steam engines which move in a simulated environment with watering holes, gasoline stations, quarries etc..  In order to be able to move, the agents need both gasoline and water which are to be found in different places, however.


Fig. 12: Schematic structure of the PSI system (Dörner and Schaub, 1998, p. 7)

A PSI agent has a set of needs which are divided in two classes:  material needs and informational needs. Among the material needs are the intake of fuel and water as well as the avoidance of dangerous situations (e.g. falling rocks).  Among the informational needs are certainty (an expectation fulfilled) and uncertainty (an expectation unfulfilled), competence  (fulfilment of needs) and affiliation (need after social contacts).

State of lack can be likened in PSI to a container whose content has fallen below a certain threshold value. The difference between actual and desired state Dörner calls desire.  "A need signals thus that a desire of a certain extent is present."  (Dörner and Schaub, 1998, p. 10)

Such a state of lack activates a motivator whose activation degree is the higher, the larger the deviation from the desired value and the longer it already persists.  The motivator now tries to take over the action control in order to eliminate this condition by a consummational final action.  For this purpose a goal is aimed at which is known to the motivator from past experiences. 

With goals, motives also develop in PSI, which represent instances which initiate an action, direct it to a certain goal, and maintain it until the goal is achieved. 

Since several motivators always compete with one another for the action control, the system has a motive selector which decides, with the help of a fast expectation x value calculation, which motivator possesses the largest motive strength and thus is to receive the advantage.  The value of a motivator is determined by its importance (size of the deviation from the desired value) and urgency (available time until the removal of the actual condition);  expectation is determined by the ability of the agent to actually satisfy this need (probability of success). 

PSI has also a memory which consists of sensory and motor "schemata".  Sensory schemata  represent the knowledge of the agent about its environment;  motor schemata are behaviour programs.  There is no distinction between different kinds of memory in PSI. 

The action control in PSI takes place through intentions which are defined operationally as combination of the selected motive with the informations which are linked with the active motivator in the memory network.  These informations concern the desired goals, the operators or action schemata to be applied, the knowledge about past, futile approaches to problem solving as well as the plans which PSI produces with the help of heuristic procedures.  All these informations consist of neural nets;  an intention as a bundling of all these informations makes up the working memory of PSI. 

The central mechanisms of emotion regulation in PSI are the motivators for certainty and competence, thus two informational needs.  Active certainty or competence motivators elicit  certain actions or increase the readiness for it: 

"A dropping certainty leads to an increase of the extent of "background control ".  This means that PSI turns more frequently than usual away from its actual intention turns to control the environment.  Because with small predictability of the environment one should be prepared for everything (...) Furthermore, with dropping certainty the tendency to escape behaviours or to behaviours of specific exploration rises (...) Not so extreme cases of escape are called information denial;  one does simply not regard the areas of reality anymore which have proved themselves as unpredictable.  Part of this "retreat" from reality is that PSI becomes more hesitant in its behaviour with dropping certainty, does not turn to actions so fast, plans longer than it would under other circumstances, and is not as "courageous" when exploring."  (Dörner and Schaub, 1998, p. 33)

Emotions thus develop with PSI not in their own emotion module, but as a consequence of rule processes of a homoeostatic system.  Schaub expresses it in such a way:  "What we call with humans emotions, are the different ways of action organization, connected with associated motivations."  (Schaub, 1995, p. 6)


Dörner grants that a variety of emotions is not yet representable in PSI because the system  is missing a module for  introspection and self reflection.  However, this is, according to Dörner, only a matter of the refined implementation of the model and thus no problem in principle.


Dörner's model exhibits a set of similarities with other models.  Like Toda and Pfeifer, his starting point is to design a completely autonomous system without a separate emotion module.  As with Frijda and Moffat, PSI contains a central memory which is accessible to all modules for reading and modification at any time: 


"The use of common memory structures permits all processes to receive information particularly over the intentions waiting for processing.  Each subprocess thus knows, e.g., importance and urgency of the current intention." 

(Schaub, 1995, p. 6)


PSI works not with explicit rules, but is a partially connectivist system which produces emotions by self organization.


8.5. Summary and evaluation

The modeling approaches of Pfeifer and Wehrle clearly show which importance Toda's theory possesses for the construction of autonomous agents who could not survive without the control function of emotions.  In place of pre-defined emotion taxonomies "wired" into the model, both authors decides on the opposite approaches:  Their models contain only the most necessary instructions for the agent. 

While Wehrle still links certain events with hedonistic components, Pfeifer does completely away with them.  In the end, both systems show a behaviour which can be interpreted as "emotional" by an observer. 

Both models have the disadvantage that they say not all too much about emotions in computer agents - for this, a longer observation period is necessary in which the agents can develop.  Thus the problem is avoided to program emotions arbitrarily into a system;  on the other hand, a new discussion is opened over whether a behaviour which appears to an observer as emotional is also actually emotional.  Here the argument turns again into a philosophical one. 

Both approaches follow the assumption of emotions as emergent phenomena consequently to the end - with all pro and cons which result from it.

Aubé's attempt to link Toda's urges with his theoretical emotion model holds its own problems.  He correctly recognizes a set of inconsistencies in Toda's urges model and tries to eliminate these.  He places, however, his own definition of emotions as social phenomena into the foreground.  Aubé's fusion of Weiner's and Roseman's theories, which he combines then with his and Toda's approach into one, raises fundamental problems whose discussion would be too far-leading here. 


The model of Dörner, finally, is similar in many respects to the approaches of Pfeifer and Wehrle (and thus Toda):  Emotions are understood as control functions in an autonomous system.  Dörner links this approach with a homoeostatic adjustment model.  Also Dörner does not define emotions explicitly;  emotional behaviour develops due to the change of two parameters which he calls "certainty" and "competence".  In this case, too, emotional behaviour is attributed to the system only from the outside.  Dörner, too, regards emotions as emergent phenomena which do not have to be integrated into a system as as a separate module.  It remains to be seen in which direction PSI (and thus Dörner's emotion model) develops if the system receives an introspection module.


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