PERFORMANCE CHARACTERISTICS OF PLANNING ACTORS

WOUT VAN WEZEL AND RENÉ JORNA 

Faculty of Management and Organization 

University of Groningen, P.O.Box 800 

9700 AV Groningen, The Netherlands 

E-mail: w.m.c.van.wezel@bdk.rug.nl; r.j.j.m.jorna@bdk.rug.nl 

ABSTRACT. Planning is a field of interest in many scientific 

disciplines. These scientific areas cover a multitude of planning 

approaches that at first sight do not have much in common: psychophysiological analyses, organizational science, linguistics, cognitive 

science, operations research, and spatial science, to name just a view. 

The differences in ontologies and methods used make it difficult to 

make statements that transcend the mono-disciplinary perspectives. 

Still, no matter the research field, planning always concerns 

anticipating on the future and determining courses of action. As a 

consequence, there must also be similarities between the various 

approaches that deal with planning. This paper proposes a number of 

characteristics that can be used to analyze the differences and 

similarities in performance of different kinds of planning actors. 

1. INTRODUCTION

Where will we go and how do we get there? This question is 

an inherent part of intelligent systems. The ability to anticipate 

and plan is usually seen as a required and perhaps even 

essential feature of such systems. It is the fundament of goal 

directed behavior; systems that pursue goals need to take the 

future into account. 

 Planning is not a nicely ordered and well-defined subject. 

Various disciplines with various scientific backgrounds deal 

with planning, such as (cognitive) psychology, mathematics, 

economics, operations research, artificial intelligence, and 

management and organization. In our opinion, comparing, 

combining, or even integrating the research efforts of each of 

the individual planning approaches can be a fruitful next step 

in planning research in general. Due to the sheer differences 

between these scientific areas, however, it seems difficult to 

make generic statements about the relation between the 

planning of an actor and its performance. But, the fact that 

planning always concerns anticipating on the future and 

determining courses of action might provide an opening. This 

notion is used in this paper to introduce a frame of reference 

for planning. In section 2, we provide a generic and abstract 

definition of planning, resulting in a discussion of four 

different scientific planning areas. Section 3 provides a 

number of generic characteristics with which the planning 

research areas are compared. In section 4, we draw the 

conclusions. 

2. PLANNING ACTORS

As stated in the previous section, many research areas 

somehow deal with planning. In this section, we will describe 

four of such areas. This description will be based around our 

conception of planning, which can be outlined by four topics. 

 First, it is important to acknowledge that some entity must 

make the plan. Note that all kinds of entities can make plans, 

for example, humans, robots, computer programs, animals, 

organizations, etc. 

Second, someone or something must execute the plan, i.e., 

the intended future must somehow be attained. Again, this can 

be done by all kinds of entities, and the planning entities need 

not necessarily be involved in plan execution themselves. 

Third, the planning entity needs some kind of model of the 

future, since the future is essentially non-existent. This model 

should include states, possible actions of the executing entities 

and the effect of actions on the state they reside in, constraints, 

and goals. Planning and anticipation presume that such a 

predictive model is available, otherwise the chance that a plan 

can be executed as intended becomes a shot in the dark. 

 The fourth element of planning is the plan itself. The plan 

signifies the belief that the planning entity has in the model of 

the future: the actions in the plan (which are performed by the 

executing actor) will lead to the desired or intended future 

state. 

 The first and second topics lead to four kinds of planning 

actors that have their own embedding in literature: 

1. Humans that plan and execute: (cognitive) psychology 

2. Humans that plan but do not execute: organizational 

science 

3. Artificial actors that plan and execute: artificial 

intelligence 

4. Artificial actors that plan but do not execute: operations 

research 

In the remainder of this section, we will discuss these four 

kinds of planning actors. The third and fourth topic can be 

used to analyze the performance of planning actors. This will 

be discussed in the next section. 

Humans that plan their own activities: To have a closer look at 

the execution of the task, we start with cognition, where the 

study of planning contains the study of human (intelligent) 

activities (tasks). In this perspective the old definition of 

Miller, Pribram & Galanter is used, saying that a plan is a hierarchical process within an organism that controls series of 
operations [1]. Jean-Michel Hoc elaborates this definition and 

says that planning always involves anticipation and schematization [2]. What he means is that planning is a two-line 

parallel process, in which a future state is taken into account 

(anticipation) and in which a (stored) mental scheme can be 

applied if a concrete planning problem arises. Therefore, Hoc 

talks about bottom-up and top-down processes that are always 

involved in making a plan. 

 Because of the terminology he uses, Hoc is implicitly 

taking a position in the complex cognitive debate about 

planning. This debate involves two closely related topics. The 

first deals with the question whether planning is a form of 

problem solving [3] or whether planning and problem solving 

only overlap [4]. The second is about the question whether 

human planners work hierarchically [3] or whether they plan 

opportunistically or even chaotically [5]. We discuss the issues 

successively in greater detail. 

 Planning and problem solving: Newell et al. [6] describe 

the planning method as a part of a general problem solving 

technique. It consists of a reformulation of the problem in 

more abstract and restricted terms, its solution in a simplified 

problem space with another level of resolution [7], its 

retranslation into the original problem situation and 

subsequently its solution. In later papers Newell & Simon 

rename the planning method as problem abstraction, necessary 

if the problem is not solvable within its original state space [3]. 

Because planning as well as problem solving means searching 

for routes (i.e., sequences of actions that lead to a solution or a 

goal state), the explicit distinction between planning and 

problem solving disappears in the later work of Newell & 

Simon. Planning is just one very interesting example of the 

general problem solving approach. Das et al. [4] argue against 

this “planning is a subset of problem solving” approach in 

saying that a difference exists in problems to prove and 

problems to find. According to Das et al. [4, p. 40] “planning 

is a more pervasive, general regulating process than problem 

solving, with problem solving being a part of a planning 

process.” Planning includes anticipation and overview and 

refers to future actions, whereas these components seem to be 

absent in problem solving. According to us this may almost be 

a game with words, because one could state that searching and 

trying to reach a goal and constructing a problem space with 

states and operators, imply future actions and anticipation. We 

will not settle the discussion, here. Das et al., however, may 

have a point in one aspect of this debate. An enigmatic 

element in the problem solving approach of Newell & Simon 

has always been the starting point of the problem solving 

process. How does a problem solver construct a problem 

space? Where does the choice for a particular problem space 

come from? Why does a problem solver constructs this special 

problem space and not another? In terms of Newell & Simon 

the question is how a task environment gets its representation 

in a state space description. It is easy to say that one has a new 

problem here, which requires a second order state space 

description. Although this might be true in the strict sense of 

the word, it does not solve the issue. Perhaps something like 

what Das et al. called “overview” or “having a higher 

perspective” is necessary [4]. Therefore, it might be insightful 

to distinguish planning as second order problem solving from 

“ordinary” problem solving. If, in line with Newell & Simon, 

one considers the planning task in organizations and 

institutions to be a problem solving process, the question 

appears how planners construct an initial representation. Do 

they start with an overview or are they just trying? In the first 

situation there is an explicit state space to start with. In the 

second situation the state space is reformulated again and 

again. 

 Hierarchical and opportunistic planning: The discussion 

about the relation between planning and problem solving is 

closely connected to the way the planning (or problem 

solving) procedure is carried out in practice: hierarchical, 

opportunistic, or even chaotic. In the first place because the 

suggestion may be present that solving a problem with or 

without an overview is done straightforward. One just has to 

follow a couple of rules from top to bottom and one ends up 

with a solution. In the second place the issue of the overlap 

between planning and problem solving very much depends on 

the format of representations in the information processing 

system of the human planner. Do planners use production 

rules? How are these rules controlled? Or do planners use 

schemata and frames? Both issues come together in the 

discussion started by Hayes-Roth & Hayes-Roth [5] about 

hierarchical and opportunistic planning. 

 Hierarchical planning means that there is a nested number 

of goal and sub-goal structures or a hierarchy of 

representations of a plan. The highest level in the hierarchy 

may be a simplification or an abstraction, whereas the lowest 

level is a concrete sequence of actions to solve (a part of) the 

planning problem. One solves a planning problem by starting 

at the highest level and then one continues by realizing subgoals until one reaches the final solution. Hayes-Roth & 

Hayes-Roth relate this to a distinction in the overview and the 

action aspect of plans that they successively call planformation and plan-execution [5]. 

 Unjustly, but quite understandably, the hierarchical 

approach is attributed to Newell & Simon. They started to talk 

about problem solving in terms of problem spaces, goal 

hierarchies, and universal sub-goaling. We consider this 

attribution to be at least partly wrong - one only has to recall 

Simon’s bounded rationality concept - but we are not going to 

discuss the issue here [8]. 

 In a contradistinction to the hierarchical view on planning, 

Hayes-Roth & Hayes-Roth propose a so called opportunistic 

approach to planning. This non-hierarchical planning assumes 

that a plan is created with the help of some kind of mental 

blackboard where pieces of information, relevant cues, and 

possible sub-goals are stored. They claim and show that 

planning happens asynchronously and is determined by the 

momentary aspects of the problem. No fixed order of operations exists; plan creation and the steps to be taken grow out 
of the problem stage at hand. When planners solve a planning 

problem, they may start with the top-goal, but very soon they 

loose track of the goal structure and then they continue to 

fulfill the goals that are reachable within reasonable time. The 

hierarchy very soon vanishes and what remains is some sort of 

heterarchy. Therefore, this kind of planning behavior is called 

opportunistic. 

 Although the contrast with the hierarchical approach may 

be large, a strong similarity is also present. In the hierarchical 

as well as in the opportunistic approach the fundamental 

assumption is that planning is problem solving, that can best 

be described in terms of problem spaces, production rules, and 

goals. That is to say that the basic descriptive structure is the 

same for both, but that real behavior within the problem space 

is executed differently. 

 With regard to the problem space description, hierarchical 

as well as opportunistic planning differ from the perspective 

defended by Riesbeck & Schank [9]. The representation of 

planning problems is described in terms of scripts and frames 

consisting of objects, slots, and relations. The information in 

the cognitive system, necessary to make a plan, is semihierarchically structured. This means that some kind of 

representational skeleton or framework is retrieved from 

memory. Stored plans contain guidelines for resolution of sorts 

of problems. In this process two stages exist. First a skeleton 

plan is found, and second the abstract steps in a plan are filled 

with concrete operations. Although general cognitive 

processing is involved in making a plan the emphasis in this 

approach is on the memory system. Plans at different levels of 

abstraction and in different formats are stored in and retrieved 

from memory. There are strong similarities with the approach 

to planning that Hoc proposed [2]. 

 It is very difficult to reveal the different mental 

representations planners use in solving planning problems. 

Asking them whether they use production rules or scripts is 

not reliable and might also give them a cue. 

 From the above discussions, we derive the following 

conclusions. Together, the paradigms that were discussed 

provide various interpretations of a cognitive approach to 

human planning. In this approach, planning is about how to 

find the actions that solve a problem or, more general, reach a 

goal. The process of planning is not neatly hierarchical but 

switches in level of abstraction and in the time frame under 

consideration. The process itself is about formulating goals, 

finding similar solved goals, finding existing plans, adapting 

plans, and storing plans in such a way that they can easily be 

found for future reference. 

 There is another kind of planning that is performed by 

humans. Instead of planning ones own activities, humans can 

be involved in coordinating the activities of others. Typically, 

this takes place in organizations, and we make the shift from 

planning and executing your own activities to planning of 

activities that are executed by others. 

Humans that plan organizational processes: Planning is a 

phenomenon that occurs at multiple places in an organization. 

In its most abstract sense, all activities that involve the 

determination of the future of the organization are dealing with 

planning. This includes strategic considerations that determine 

“where the organization must stand” in 10 years, less abstract 

issues such as growth targets or product innovations, but also 

very concrete decisions such as who will work at what time 

next week, or the exact production times and machine 

allocations of the production for the following week [10,11]. It 

is the type of planning about concrete entities that we 

primarily focus upon. That kind of planning is about 

coordination of activities of organizational members and the 

allocation of resources [12]. The types of activities and 

resources vary widely over organizations. A rough 

categorization that is based on the things that are planned is 

the distinction between production planning (machines, orders, 

machine operators), staff planning (shifts, personnel), and 

transportation planning (vehicles, routes, chauffeurs, 

shipments) [13]. 

 Although the variety in organizational planning problems 

seems large, there are also many characteristics that are shared 

by organizations. A first generic characteristic for planning 

problems in organizations is that it basically concerns the 

coordination of supply and demand, whereby (a) the supply 

consists of scarce capacity and (b) the way in which this 

capacity is put to use can make a difference with respect to the 

goals in the organization [13,14,15]. Examples are producing 

at low costs at a production facility, having enough phone 

operators at a call center, or taking care that all employees 

work the same amount of night shifts. A second shared 

characteristic is that the planning process is distributed over 

multiple human planners. This means that plans get made in 

parallel, and that coordination between the plans is needed. A 

third shared characteristic for all organizational planning 

activities is that they are organized hierarchically. Planning 

problems in organizations are too complex to be solved by one 

person, so some kind of division in sub-problems is necessary. 

Therefore, there are approximate plans for the long term and 

detailed plans for the short term. This induces the need to 

coordinate; plans at higher hierarchical levels define the 

decision space for lower hierarchical levels. An example of 

such a hierarchy is strategic planning versus rough capacity 

planning versus production scheduling. 

 Not much literature or theory exists about the relations 

between the planning domain, the planning task, the 

organization of the planning, and the performance of plan 

execution. Most analyses are limited to task models, for 

example, McKay et al. [16], Mietus [17], Dorn [18], and 

Sundin [19]. Lack of a theory to explain the relation between 

planning complexity, planning organization, task performance, 

and planning support makes it is difficult to pinpoint the cause 

of the planners’ discontent, to attribute the causes of poor 

organizational performance to the planning, or to analyze and 

design planning practices. For example, the cause of poor 

factory performance can be the mere impossibility of matching 
the requirements (e.g., there is not enough capacity available 

to meet the demands), the clumsiness of the organization of the 

planning, the inadequacy of the human planner to solve 

complex problems, the absence of specialized planning 

support in practice, or a combination of these factors. 

 In order to make generic statements about the planning 

task, it is important to know what the task performance 

depends upon (notice that by performance we mean execution 

without a qualitative connotation). According to Hayes-Roth 

& Hayes-Roth, the determinants of the planning task are 

problem characteristics, individual differences, and expertise 

[5]. That the task performance depends on individual 

differences and expertise is no surprise. This applies to all 

tasks. But the fact that the task performance also depends on 

problem characteristics leads to the statement that it is possible 

to describe a planning problem, at least partly, independent 

from the planner. 

 Clearly, approaches of planning your own activities deal 

with other questions than approaches for organizational 

planning. In section 3, we will analyze in what way planning 

of organizations differs from planning of your own actions. 

First, however, we will look at planning by artificial agents. 

Artificial agents that plan their own activities: Artificial agents 

such as (simulated) robots that plan their own behavior need to 

be able to deal with uncertainty and incomplete information in 

their task environment. For such agents, planning is a means to 

reach the goal, just as it is with human problem solving. Due 

to the close resemblance of human and artificial agents, 

planning of artificial agents is very much related to the 

problem solving approaches as described earlier. Techniques 

from Artificial Intelligence are used to let such agents function 

more or less independently in their environment, and react on 

unforeseen events [20,21,22]. Much of the planning research 

in Artificial Intelligence stems from the wish to let 

autonomous actors or agents (such as robots) perform tasks 

without prescribing how the task should be carried out [23]. 

Most Artificial Intelligence methods, whether they are called 

algorithms, procedures, or heuristics, are based on state space 

descriptions. An agent or actor finds himself in a state, in 

which it can perform a limited number of actions. An action 

changes the state, after which it can again perform a number of 

actions [24]. The agent keeps on choosing and performing 

actions until the state it gets in somehow satisfies its goal. 

Planning is one way in which the agent can reach its goal. 

Other ways are, for example, trial and error or full search. To 

make a plan, an agent somehow anticipates the future by 

simulating the actions he will make. This requires the 

existence of (internal) representations. The original link to 

physical entities has been relinquished somewhat so planning 

agents are now often only computer programs that find a plan 

merely for the sake of research, and therefore not necessarily 

execute it. In this paradigm, planning is searching for a 

sequence of actions that will bring the agent from its current 

state in the goal state. Models of human problem solving, 

which were discussed in the previous subsection, have 

provided researchers in Artificial Intelligence with starting 

points for the planning functions of their artificial agents. 

Many examples are based on the initial General Problem 

Solver (GPS) of Newell & Simon [3], which constructs a 

proposed solution in general terms before working out the 

details, the opportunistic planning paradigm, and script-based 

planning. Here it becomes clear that models of human problem 

solving are closely related to the anticipation and planning of 

artificial agents. 

Machines (computers) that plan organizational processes: A 

lot of planning research deals with automatically finding (or 

generating) plans for future organizational processes. Usually, 

this is about making a quantitative model that can search 

efficiently for good solutions. At first glance, the same kind of 

reasoning is used as in cognitive sciences: a problem space is 

set up and the aim is to find a state that satisfies all constraints 

and scores well on goal functions. The states are (just like in 

the cognitive problem solving approaches) transformed by 

operators. The difference is that states and operators comprise 

something else than the ones in cognitive science, namely 

values on variables and mathematical operations [25]. 

 Models exist for all kinds of processes such as routing of 

trucks, staff scheduling, job shop scheduling [26,27], and flow 

shop scheduling. Some of the scientific fields that deal with 

this kind of research are Operations Research (e.g., linear 

programming, nonlinear programming, all kinds of heuristics), 

and Artificial Intelligence (constraint satisfaction programming, genetic algorithms). Although the approaches of course 

differ, they also possess common characteristics. They are 

based on an analysis of the entities that are scheduled. For 

example, to make an algorithm for a planning problem in a 

flow shop one must know the capacities of machines, setupand cleaning times, the number and sizes of orders, the 

processing characteristics, etc. All these characteristics can be 

used to determine the best way to navigate through the 

problem space of possible solutions. An example of how such 

knowledge can be used in an algorithm is to start to plan on 

the bottleneck first, because it is often the sensible thing to do 

in order to avoid problems in a later stage of the planning 

process. Most techniques are somehow limited in the kinds of 

characteristics that they can handle. For example, a linear 

programming model cannot deal with nonlinear constraints, 

and temporal reasoning is tacky to implement in many 

mathematical techniques. Therefore, the domain analysis must 

be translated in the quantitative model, and the solution must 

be translated back to the application domain [28]. 

 Computer programs that create schedules are rarely used 

on their own. The fact that information is lost during 

abstraction and translation of the domain into the model is 

widely recognized. For that reason, mathematical solution 

techniques are usually used in the context of decision support 

systems, where a planner can manipulate and change a plan 

manually so he is not bound to the solution that is presented by 
an algorithm. 

 As with the distinction between humans that plan for 

themselves and humans that plan for organizational processes, 

the approaches that deal with computer programs that plan for 

their own actions differ from approaches that deal with 

computer programs that plan for organizational processes. The 

differences have to do with the characteristics of the actors and 

will be analyzed in the next section. 

3. ANALYSIS OF THE PLANNING ACTORS

In section 2, we discussed four topics that are relevant for 

planning in general: (1) an entity must make the plan, (2) an 

entity must execute it, (3) the entity that makes the plan must 

have a model of the future, and (4) the plan exemplifies the 

ability of the planning entity to use the model of the future to 

lead the executing entity to the goal state. We now have nearly 

all the ingredients available to make a reasonable comparison 

between the different perspectives on planning. The goal of 

this comparison is to gain insight in the limitations that the 

approaches for the respective perspectives have and to see 

where those limitations come from. In the end, this should lead 

to a better understanding of the “planning” phenomenon, and 

perhaps the respective approaches can learn from each other. 

In section 2, we used topics 1 and 2 to describe four distinct 

planning areas. Topic 3 and 4 are used to assemble a number 

of characteristics with which the performance of planning 

actors can be analyzed and compared. The aspects that will be 

discussed in detail and that are used for comparisons are: a) 

the way in which the approaches deal with complexity; b) 

closed versus open world assumptions; c) the information 

processing mechanism and its architectural components such 

as memory and attention; d) the representations; e) 

communication, meaning, and interpretation; f) the 

characteristics of coordination; and g) aspects of execution of 

the plan. 

Complexity reduction: Planning problems are assignment 

problems for which a limited set of structurally similar 

solutions exist. Theoretically, all solutions of a planning 

problem can be calculated in order to choose the best solution. 

Unfortunately, even the most seemingly simple planning 

problems are transcomputational [29], which means that 

enumeration of solutions is not practically possible. To 

overcome this, planning actors must choose a way to somehow 

look at a limited number of viable solutions. Indeed, many of 

the differences between the kinds of planning actors (see 

section 2) can be explained by the way in which they reduce 

the complexity of their planning problems [7]. Some ways in 

which a planning actor can deal with the complexity are: 

1. Opportunistic planning. The planning actor takes 

decisions without any structure; only momentary aspects 

matter [5]. 

2. Plan partitioning. Plans are often multi-dimensional. The 

search space can be limited by first making plans for the 

individual dimensions, and then putting the plans 

together [30]. An example is a production plan that 

coordinates machine operators, machines, and 

production orders: separate plans can be made to assign 

machine operators to machines, and orders to the 

machines. 

3. Multiresolutional planning. A plan can be made at 

multiple levels of resolution. The plan at a low level of 

granulation will have a lower complexity. This plan will 

constrain the search space of the plan at a higher level of 

granulation [7], so the total number of to be assessed 

alternatives is lessened. 

4. Learning. Different plans can contain similar structures 

that can be reused in similar circumstances. (The abstract 

connotation is intended since learning can be based on a 

wide variety of aspects of the planning process.) 

In both plan partitioning and multiresolutional planning, 

multiple plans are created. This decomposition must be a 

closed-loop process [7, p 265], i.e., the plans must together 

provide a complete plan. This is of special interest for multiagent planning systems (such as an organization), where the 

plans can be made by different people and the coordination 

issue arises. Furthermore, each of these multiple plans is a 

(possibly complex) plan in itself, and can therefore be subject 

to each of these four strategies recursively. 

"Closed world" vs. “open world”: Looking from a generic 

perspective, the planning task itself can be called a synthetic or 

configuration task. From a task perspective realizing a suitable 

plan or solving a planning problem requires three nearly 

decomposable phases. In state space descriptions the first 

phase is the design of a (complex) initial state, of goal state(s) 

and of admissible operations to change states. The second 

phase is given the admissible operations to search for an 

(optimal) solution. In many cases search does not give an 

optimal solution. The most one may get is a satisfying solution 

and even that is often not possible. Then, the third phase starts 

in which one goes back to the initial state and the admissible 

operations and changes these in such a way that a solution is 

found. Formulated in other words, the phases of (1) initial 

state, (2) search, no solution, and (3a) start again with a new 

initial state follow the so-called "closed world" assumption. 

This is the necessary sequence if algorithms are applied. 

However, there is another way of dealing with the third phase 

which is more usual, especially if humans have to make a plan. 

If the second phase does not give an optimal or satisfactory 

outcome given the constraints and goal functions, the planner 

already is so much involved in the planning process, that 

because he has a glimpse of the solution given the constraints, 

he takes his ”idea” of a solution for compelling. He therefore 

changes the initial state and the admissible operations, that is 

the constraints, in such a way that they fit the preconceived 

solution. This order of phases can be named the "open world" 

approach. It consists of (1) initial state, (2) search including 
not finding a real or established fixed solution, and (3b) 

adjustment of initial state according to the “fixed” solution 

reality. This sequence of activities is what human planners 

whether in the industry or doing errands frequently and with 

great success do, but formalizing such knowledge for use in a 

computer program or robot seems to be very difficult. 

Information processing mechanism and architectural 

components: An information processing mechanism embodies 

the way information is selected, combined, created, and 

deleted. The mechanism itself needs a physical or 

physiological carrier. Various possibilities are already present, 

such as the brain as our neurological apparatus, the layered 

connection system of a chip in a computer, a human individual 

in an organization, or a group of interconnected individuals in 

an organization. The most relevant distinction is the one in 

internal and external mechanism. With internal we mean that 

there is no direct access to the system from outside. Internally 

controlled, but not directly visible processes take place in the 

system. The cognitive system and the chip are internal, but 

they differ in the sense that the latter is designed which means 

that its operations are verifiable. External are information 

processing mechanisms such as groups of individuals or 

organizations. In other words, the kind of predictive model 

that is needed to anticipate ones own actions differs from the 

kind of model that is needed to anticipate actions of others. 

With respect to planning, this distinction is of course relevant 

if one realizes that if the plan needs to be communicated, a 

translation is necessary between the physical carrier and the 

receiver, which must be reckoned with during planning. This 

is the case when a planner makes a plan that is executed by 

others. 

 An architecture is a set of components of which the 

arrangement is governed by principles of form or function 

[21]. A cognitive architecture consists of memory components, 

of attention processors, of sensory and motor components, and 

of various kinds of central processors. The division is by 

function and the components are all implemented in 

neurological structures in the brain. Two other material 

structures for architectural layout are the chip and the 

constellation of a group of individuals. The same kind of 

components can be discerned for the computer, consisting of 

memory, sensory and motor components, and central 

processors. For a group of individuals the architecture is 

different because the constituting elements are similar as for 

the individuals, but the roles and tasks are different. Again, the 

discussion about the character of the architecture boils down to 

a discussion about internally or externally defined. Internal is 

the cognitive architecture, whereas chips and groups of people 

can be dealt with externally. 

 (Internal) representations: In cognitive science the conceptual 

framework to deal with representations can be found in the 

approaches of classical symbol systems, connectionism, and 

situated action [31,32,33,34,8]. The basic idea is that humans 

as information processing systems have and use knowledge 

consisting of representations and that thinking, reasoning, and 

problem solving consist of manipulations of these 

representations at a functional level of description. A system 

that internally symbolizes the environment is said to have 

representations at its disposal. Representations consist of sets 

of symbol structures on which operations are defined. 

Examples of representations are words, pictures, semantic 

nets, propositions, and temporal strings. A representational 

system learns by means of chunking mechanisms and symbol 

transformations [32]. An entity that makes a plan for itself can 

of course misinterpret its position in the environment, for 

example because it cannot represent its environment or 

because it cannot manipulate its representation of the 

environment adequately. Furthermore, an entity that makes a 

plan for others can additionally have this problem with respect 

to the entities that must execute the plan. Representations are 

also immediately relevant for anticipation. A description of a 

future state in whatever symbol or sign system is the core of 

any discussion on anticipation. Rosen, for example, defines an 

anticipatory system as “a system containing a predictive model 

of itself and/or its environment, which allows it to change state 

at an instant in accord with the model’s predictions pertaining 

to a later instant” [35]. Someone who makes a plan for an 

organization does not need a model of itself, but a model of 

others and their environment. This can complicate 

communication and interpretation of the to be planned system. 

Communication, meaning, and interpretation: Communication 

means the exchange of information between different 

components. Depending on whether we are talking about 

internal or external information processing entities, 

communication means restrictions on the kinds of symbols or 

signs that are used for the exchange. If we relate this to the 

before mentioned discussion about representations, the various 

kinds of signs have different consequences. Clearly, sign 

notations are more powerful, but also more restricted than sign 

systems, which in turn are more powerful than just sign sets 

[36,8,37]. Unambiguous communication requires sign 

notations, but we know that all communication between 

humans is not in terms of notations. If computers require sign 

notations and humans work with sign systems, then if the two 

have to communicate, the one has to adjust to the other. Until 

recently, most adjustments consist of humans using notations. 

Now, interfaces are designed that allow computers to work 

with less powerful - in terms of semantic requirements -, but 

more flexible sign systems. This means that computers can 

deal with ambiguity. For mental activities no explicitness 

(channels, codes etc.) is necessary; for planning as an external 

task it is essential. 

Coordination: Coordination concerns attuning or aligning 

various entities that are not self-evident unities. Information 

processing in a cognitive system is a kind of coordination 

mechanism (with no direct access). It is internal or mental. The 
Natural actor Artificial actor

Self planning Organization 

planning Self planning Organization planning

Complexity reduction 

Plan partitioning; 

Opportunistic planning; 

Learning 

Plan partitioning; 

Multiresolutional 

planning 

Plan partitioning; 

Learning Plan partitioning 

Close vs. open world Fixing the reality to the solution that is found; 

reformulate the starting-point 

Searching for a solution that fits the (modeled) 

reality 

Information processing mechanism

Information processing 

needs not to reckon with 

the outside world 

Translation of internal 

internally coded 

information is necessary

Information processing 

needs not to reckon with 

the outside world 

Translation of 

internally coded information is necessary; 

designed 

Architectural 

components

Neurological: memory structures, attention 

processors 

Electronic: memory 

structures, attention 

processors 

Program components: 

procedures, variables

Representations Self-representation Representation of others Self-representation Representation of 

others 

Communication, 

meaning, and 

interpretation

Mostly communication 

with sign systems or sign 

sets 

Communication with 

sign notations 

Coordination Only with respect to 

anticipated actions 

Coordination of actions 

of others 

Only with respect to 

anticipated actions 

Coordination of actions 

of others 

Planning, execution, 

and control Intertwined Separated Intertwined Separated 

TABLE 1: Characteristics of kinds of actors related to what they are planning and for whom

coordinating processor is cognition itself. No explicit code is 

necessary. If the code is made explicit and obeys the 

requirements of a notation, then we can design an artificial 

intelligent agent that in its ultimate simplicity could be a chip. 

In case of a set of entities that not by themselves are a coherent 

unity, various coordination mechanism can be found, such as a 

hierarchy, a meta-plan, mutual adjustment, a market structure, 

and many others [38,39]. The important difference with the 

single agent is that these coordination mechanisms are external 

and of course with direct access. 

Planning, execution and control: Making a plan, executing it, 

and monitoring its outcomes in reality are valued differently in 

planning your own actions and in planning actions of others 

(i.e., organizational processes). The planning in organizations 

usually is decoupled from the execution of the plan. There are 

two main reasons why the planner is someone else than the 

one who executes the plan. First, planning is a difficult job that 

requires expertise and experience. This is the organizational 

concept of task division. Second, a planner must be able to 

weigh the interests of many parties. Therefore, he must have 

knowledge about things that go beyond the limits of the 

individual tasks that are planned. The consequence of this 

decoupling is almost always inflexibility with respect to 

adaptation. For errand tasks the possible division in terms of 

sub-tasks may be interesting, but can in reality be intertwined 

with flexible adaptation after unforeseen events. If the 

controlling entity is itself a unity, discussions about transfer, 

communication, sign systems to do the communication, and 

representations are almost trivial. This does not make the

planning task itself simpler; it only prevents the occurrence of 

ambiguity, interpretation, and meaning variance. 

 We now have discussed a number of planning approaches 

in section 2, and a number of generic planning characteristics 

in this section. Table 1 summarizes the findings. Evidently, 

measures to relate the performance of an actor to its planning 

activities are context dependent, because the differences in 

contexts can make performance measures incomparable. For 

example, the uncertainty in the task environment of an actor 

makes it hard to establish a clear cause-effect relation between 

plan and execution. Still, cross functional analyses add to our 

understanding of the mono-disciplines, and can help to get a 

better understanding of the relation between planning and 

performance. 

4. CONCLUSION

Planning is a much debated, highly controversial, and multifaceted issue. We stated that various kinds of actors can be 

discerned: natural, artificial, and collective actors. We also 

discussed that there is no easy exchange between the various 

planning approaches. Management and organization, cognitive 

science, mathematics, artificial intelligence, and economics, 

although all are discussing important issues in planning, do not 

start with the same problem formulation. We approached the 

issue of planning by looking especially at the entity that makes 

a plan. By looking at the kind of actors, their characteristics, 

and the level of description of the entities and components 

involved, we stated that discussions about the relation between 

planning and performance do not have to end in controversies 

and avowed misunderstandings. We have sketched the 
components and ingredients of planning actors and we showed 

that comparisons can be made and that positions can be 

clarified. 

 Are there good reasons to discuss planning issues in 

greater detail? We think there are two good reasons. The first 

is that any planning (or weaker: any anticipatory) system 

ultimately acts in an open world. There is nothing wrong with 

the closed world assumption, but in the end it is part of an 

open world. Switching between open and closed worlds is 

something human information processing can easily do, but it 

is difficult to get it realized for artificial (software) and 

collective systems (organizations). The second reason is that 

whether we like it or not, more and more of our fellow 

“intelligent” companions are software actors (agents) and we 

are interacting with them. Artificial and collective actors are 

also planning, but something seems to be different. This 

incompatibility cannot be solved by imposing one approach on 

all kinds of actors. It can only be realized if we know what 

precisely natural actors do when they make plans. 

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