Formal Goal-based Modeling of Organizations
Viara Popova
and Alexei Sharpanskykh
De Montfort University, Centre for Manufacturing, The Gateway, Leicester, LE1 9BH, U.K.
Vrije Universiteit Amsterdam, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands
Abstract. Each organization exists for the achievement of certain goals. To
ensure continued success, the organization should monitor its performance
w.r.t. these goals. Performance is often evaluated by estimating performance
indicators (PIs). In most existing organization modeling approaches the relation
between PIs and goals is implicit. This paper proposes a formal approach for
modeling goals based on PIs and defines mechanisms for establishing goal
satisfaction, which enable evaluation of organizational performance. Analysis
and methodological issues related to goals are briefly discussed.
1 Introduction
The behavior of an organization is usually guided by its strategic and tactical goals.
The performance of an organization is often evaluated by estimating the values of its
qualitative and quantitative performance indicators (e.g., profits, number of clients).
To ensure effectiveness of an organization, the key PIs should be reflected in its
goals. In most existing approaches on organization modeling the relation between PIs
and goals remains implicit. This paper defines a clear and general mechanism for
specifying goals based on PIs. The organization’s performance can then be evaluated
by estimating the satisfaction of its goals. Moreover, often the satisfaction of goals
can only be established in a framework, in which goals are related to other concepts
(such as tasks, roles and agents). A goal-based modeling approach proposed in this
paper constitutes a part of a general organization modeling and analysis framework, in
which organizations are considered from four interrelated perspectives (or views). In
particular, the performance-oriented view addresses PIs, goals and relations between
them; the process-oriented view considers tasks, workflows and resources; the
organization-oriented view defines roles, their authority and interaction relations; the
agent-oriented view identifies different types of agents with their capabilities, and
principles of allocation of agents to roles. The views are linked via relations between
their concepts, e.g., agents are allocated to roles, the roles are assigned tasks, tasks
realize goals, etc. Concepts and relations within every view are formally described
using dedicated languages expressive enough to convey structures and processes of
organizations of most types. To provide the formal meaning for the concepts and to
ensure consistency of specifications, axioms and constraints are defined that establish
relations between concepts within and across views.
Popova V. and Sharpanskykh A. (2008).
Formal Goal-based Modeling of Organizations.
In Proceedings of the 6th International Workshop on Modelling, Simulation, Verification and Validation of Enterprise Information Systems, pages 19-28
DOI: 10.5220/0001735800190028
The formal language, axioms and constraints specific for modeling goals within the
performance-oriented view are described in this paper. Moreover, some of the
verification techniques specific for the performance-oriented view are briefly
considered. Furthermore, the paper addresses methodological issues of creating and
revising goal structures, and considers the process of organizational performance
evaluation based on goals.
The proposed approach was applied for modeling and analyzing an organization
from the security domain within the project Cybernetic Incident Management. The
main purpose of the organization is to deliver security services. It has multi-level
structure, with predefined job descriptions for employees. The examples given in this
paper are related to a part of the organization concerned with planning the allocation
of security officers to locations of customers. The planning process consists of
forward (or long-term) planning and short-term planning. Forward planning is a
process of creation, analysis and optimization of forward plans for the allocation of
security officers for a long term. During the short-term planning, plans for the
allocation of security officers to locations within a certain area for a short term are
created and updated based on the forward plan and up-to-date information about the
security employees. Due to the space limitations only some parts of this case study are
given in the paper. A more elaborated description of the case study is given in [7].
The paper is organized as follows. In Section 2 the main concepts for the goal
modeling framework are specified. The relationships between them are described and
formalized using the dedicated logic-based language in Section 3. Section 4 discusses
how the performance of the organization can be evaluated. Some design principles are
given in Section 5. Section 6 discusses the related work on goal-oriented modeling.
2 Goal Modeling Concepts
Each organization exists for the achievement of one or more goals. This varies
depending on the type of organization, e.g., the main goal of a manufacturing
company can be the realization of maximal profit. Being aware of these goals is a
prerequisite to taking measures for their satisfaction. To ensure continued success, the
organization should monitor its performance w.r.t. formulated goals. To enable goal-
based performance evaluation, organizational goals should be formulated over
performance measures (indicators).
Performance Indicators (PIs) are defined as measures, quantitative or qualitative,
that can be used to give a view on the state or progress of the company, a unit within
the company or an individual (e.g., time to produce a short-term plan, efficiency of
allocation of security officers). Expressions can be formulated over PIs containing
>, =
<, for example for defining target values: an expression over the PI P1:“efficiency of
allocation of security officers”
is defined as P1 = high.
Goal Pattern is a property over one or more PI expressions used to define a goal. A
goal pattern can be checked for a given state/time point or interval for a company or
agent. Goal patterns have a
type specifying the way the property will be checked:
achieved (ceased) – check if the property is true for a given time point; maintained
(avoided) – check if the property is true for the duration of a given time interval;
optimized (maximized/ minimized/approximated) – check if the value of the PI expression
has increased/decreased/approached a target value for the given time interval.
Achieved, ceased, maintained, avoided are used on PI expressions that are evaluated to
Boolean values;
optimized is used on PI expressions that are evaluated to value of any
ordered type (maximized/minimized) or for which a distance measure is defined
(approximated). Examples: “maintained efficiency of allocation of security officers = high” with
maintained pattern type and “achieved that time to produce short-term plan 48” with achieved
pattern type. Goals are formulated by adding to goal patterns information such as
desirability and priority.
Goal is an objective describing a desired state or development of the company or an
individual. Example:
“it is required to maintain high efficiency of allocation of security officers”. A
goal is characterized by evaluation type, horizon, ownership, hardness, priority,
negotiability, etc
. Evaluation type determines if a goal is based on goal pattern of type
achieved or ceased (achievement goal), or if a goal is based on goal pattern maintained,
avoided or optimized (development goal). Horizon specifies within which time interval
(for development goals) or at which time point (for achievement goals) is the goal
supposed to be satisfied:
long-, mid-long- or short-term goal. Ownership can be
organizational (of an organization/unit/role) or individual (of an agent). Goals of agents
may comply with organizational goals or not.
Priority is defined by the numerical
estimation between 0 and 1; alternatively {very high, high, medium, low, very low} or a (partial)
ordering on goals may be defined. Normally, organizational goals have a higher level
of priority. Priority of individual goals depends on the company policy: one company
might assign lower priority to individual goals; another might decide to involve and
motivate the agents by taking into account their goals and avoiding conflicts between
individual and organizational goals. By
negotiability goals are divided into non-
negotiable and negotiable. This can be used for conflict resolution at the design phase.
Hardness distinguishes soft and hard goals. Satisfaction of a soft goal cannot be
clearly established. We use the term
satisficing to indicate acceptable degree of
satisfaction of soft goals. Labels are given corresponding to the degrees of
satisficing/denial with a natural order:
satisficed > weakly_satisficed > undetermined >
weakly_denied > denied. Satisfaction of hard goals can be established quantitatively. Hard
goals also have labels ordered as follows: satisfied > undetermined > failed. In the example
G3 is soft, PI “efficiency of allocation” cannot be objectively established to be
maintained high or not, instead we use a subjective estimation of degree of satisficing.
G3.1 is hard – it can be checked if PI “time to produce up-to-date plan” is at most 48 hours.
Goals are realizable by organizational tasks. A task represents a function performed
by role(s). A role is characterized by a set of functionalities performed by it. Roles are
allocated to agents to perform tasks. Roles and agents are committed to certain goals.
Besides organizational goals, an agent may pursue its own individual goals that
comply or conflict with organizational goals. These and other concepts are only
briefly discussed in this paper and are considered in the descriptions of other views.
Goal name:
Goal name: G3
Informal definition: It is required to maintain high
efficiency of allocation of security officers to objects
Informal definition: It is required to achieve that
within 48 hours from receiving operational data,
an up-to-date short-term plan exists
Eval. type: development goal (maintain goal pattern) Eval. type: achievement goal (achieve goal
Horizon: long-term; Ownership: organizational Horizon: short-term; Ownership: organizational
Hardness: soft Hardness: hard
Priority: high; Negotiability: negotiable Priority: medium; Negotiability: negotiable
3 Formal Goal Modeling
To specify the meta-model for the performance-oriented view the first order sorted
predicate language is used (the graphical representation of the developed meta-model
is given in [7]). In this language, for each concept a special sort is introduced, which
contains all the names of concept instances (e.g., sort
GOAL contains all names of
goals). Using this dedicated language a number of relations between goals and other
concepts are specified. To provide the formal meaning for the introduced relations
and to ensure the consistency and integrity of goal-based specifications, axioms and
constraints are defined along the definitions of relations.
The relations on goals and PIs introduced informally in Section 2 are formalized as
is_based_on: GOAL_PATTERN × PI: the goal pattern in the first argument is defined over
the PI in the second argument;
uses: GOAL_PATTERN × PI_EXPRESSION: goal pattern
defined over PI expression. For example, the goal pattern GP1 maintained efficiency of
allocation of security officers to objects = high
is based on the PI P2 efficiency of allocation of
security officers to objects
and uses the PI expression PE1 formulated over P2, (PE1: P2=high):
is_based_on(GP1,P2); uses(GP1,PE1)
is_formulated_over: GOAL × GOAL_PATTERN: The goal is defined over the goal pattern.
For example, goal
G3 defined earlier is formulated over the goal pattern GP1.
Goals are related to tasks, roles and agents by the following relations:
GOAL × TASK_LIST: the goal in the first argument is realizable by the list of tasks in the
second argument; is_committed_to: ROLE × GOAL: the goal is an organizational goal and
the role is committed to the satisfaction of this goal; wishes: AGENT × GOAL: the goal is
an individual goal of the agent. For example role
Planner is committed to goal G3.1
realizable by task T4.4.1:“update short-term plan”, i.e. is_committed_to(Planner,G3.1) &
is_realizable_by(G3.1,L41) & is_in_ task_list(L41,T4.4.1)
, where is_in_task_list: TASK_LIST × TASK.
A goal structure can be built by refining high level goals and by aggregating lower
lever goals into higher level goals. Since goals in the modeling framework can be of
two types: hard and soft, having very different features, different types of refinement
relations should be considered.
Hard goals are refined into and-lists of hard goals (sort
AND_GOAL_LIST), in which
the goals are connected by and relation. A refinement of a hard goal is specified by:
is_refined_to: GOAL × AND_GOAL_LIST: Defines a refinement of a hard goal into a list of
hard goals, which contribute to its satisfaction. When all the goals in the list are
satisfied then the goal in the first argument is satisfied as well. If one or more goals in
the list fail and no other refinement exists where all goals are satisfied, then the goal
in the first argument will fail too. More formally, the predicates
satisfied: GOAL and failed:
express the satisfaction state of a goal and the following axioms are formulated:
is_refined_to(g, l) & (gi:GOAL is_in_ goal_list(gi,l) satisfied(gi)) satisfied(g)
l: AND_GOAL_LIST (is_refined_to(g, l) gi: GOAL is_in_ goal_list(gi, l) & failed(gi)) failed(g)
where is_in_goal_list: GOAL × GOAL_LIST expresses that a goal is in a goal list. Sort
GOAL_LIST is a supersort of AND_GOAL_LIST, which contains the names of all goal lists.
When more than one refinements are defined, they are considered as alternatives
connected by
OR, i.e., they allow a choice, which measures to take to satisfy the goal.
Examples of refinement of hard goals are provided in [7].
Since the satisfaction of soft goals cannot be established in a clear-cut way, their
refinement differs from the refinement of hard goals. Instead of decomposition, we
talk about positive contribution from other goals to the satisfaction of the goal. Such
contribution can vary in its degree expressed by the following relations, in which the
second argument is a soft goal and the first argument can be soft or hard. In
GOAL × GOAL the first goal strongly contributes positively to the satisficing of the
second goal. If it is satisfi(c)ed and no other influences are known then the second
goal is considered satisficed. In
contributes_to: GOAL × GOAL the first goal contributes
positively to the satisficing of the second goal, but might not be enough to satisfice it.
The precise meaning of these relations is defined through the propagation rules for
goals refinement. These rules are used to determine the degree of satisficing of a
higher level goal (specified by a label) based on the available information about the
degrees of satisfaction/satisficing of lower level goals in its refinement list. To
determine the label of a higher level goal, first the labels of its contributing (lower
level) goals are propagated, taking into account the types of the links by which the
lower level goals are connected to the higher level goal. The propagated labels of the
lower level goals are determined using Table 1. Then, the propagated labels of the
lower level goals are combined depending on the type of the list to determine the label
of the higher level goal.
Lower level goals can be combined in lists using and- or balanced contribution
relations, contributing positively to the satisficing of higher level soft goals:
has_influence_from: GOAL × GOAL_LIST: The goals in the list contribute positively to the
satisficing of the soft goal in the first argument. For each goal in the list it is defined
separately what the level is of its contribution (the type of the link) using the above
defined relations
satisfices and contributes_to.
The combination of goals in an and-list implies that if all goals in the list are
satisfi(c)ed then the higher level goal will also be satisficed. In order to ensure this the
following constraint is enforced: at least one of the goals in an and-list is connected
with a link of the type
satisfices to the higher level goal. When lower level goals are
combined in an and-list, the label of a higher level goal is defined by the minimal
label propagated from the goals in this list using the defined order between the labels.
Another kind of relation between goals represents balanced contribution which
gives us the possibility to describe more fine-tuned ways of contributing which favor
the majority influence. The rule that is used to calculate the exact effect first
quantifies the propagated labels of lower level goals and then takes the (weighted)
average which is then discretized again to the closest label, which is the sought label
for the higher level soft goal. The quantification scale for the propagated labels may
look as follows:
satisficed = 2, weakly_satisficed = 1, undetermined = 0, weakly_denied = -1, denied = -
. Then, to fine-tune influences that the lower level goals from the balanced list (and
thus, the propagated labels) have on the label for the higher level goal, weights can be
assigned for the lower level goals in the list. Let the quantified propagated labels from
the goals in the balanced list be
and the weights defined for each goal in the list are
. Then the influence of the balanced list on the higher level goal is calculated using a
formula of the type:
Σ w
/ Σ w
. The weight of a goal in a balanced list is specified by:
has_weight_in_list: GOAL × INTEGER × BAL_GOAL_LIST. In the following an example from
the case study of a soft goal refinement by a balanced list is considered. Examples of
refinement of soft goals by and-lists are provided in [7].
Table 1. The table for determining propagated labels for a higher level goal based on the
satisfaction/satisficing labels of lower level goals and types of contributing links.
Contributing goal label \ Type of link satisfices contributes_to
satisficed/satisfied satisficed weakly_satisficed
weakly_satisficed weakly_satisficed undetermined
undetermined undetermined undetermined
weakly_denied weakly_denied undetermined
denied/failed denied weakly_denied
Example: Consider the following set of goals and relations between them (see Fig. 1):
G7: It is required to maintain optimal number of qualified personnel
G7.1: It is required to maintain high qualification of personnel
G7.2: It is required to maintain up-to-date data on the available and needed capacity and qualifications
G7.3: It is required to maintain timely recruitment and dismissal of personnel according to the data on
available and needed (human) capacity and qualifications.
Let us assume that the degrees of satisficing of the lower-level goals G7.1, G7.2 and
G7.3 are known: G7.1 is satisficed, G7.2 and G7.3 are weakly satisficed. The labels are
quantified and the degree of satisficing of G7.1 is considered 2 and of G7.2 and G7.31.
Thus the degree of satisficing of G7 is calculated as (3*2+1+1)/5 = 1.6 which we round up
2 (which corresponds to satisficed in our scale). Thus the label assigned to G7 is 2.
is_in_goal_list(G7.1, L1),
is_in_goal_list(G7.2, L1),
is_in_goal_list(G7.3, L1),
has_influence_from(G7, L1),
satisfices(G7.1, G7) ,
satisfices(G7.2, G7) ,
satisfices(G7.3, G7),
has_weight_in_list(G7.1, 3, L1),
has_weight_in_list(G7.2, 1, L1),
has_weight_in_list(G7.3, 1, L1)
Fig. 1. The refinement of the soft goal G7 into the balanced list consisting of goals G7.1, G7.2
and G7.3; and the conflict relations between G7 and the goals G8 and G9.
When a goal is refined in one list only then the influence calculated using the
described above rules defines the satisficing label of the goal. Sometimes a goal is
refined into several influence lists related by or. This reflects the knowledge that these
lists are in conflict or competition and if one is satisficed then the probability that the
rest will also be satisficed is lower. In such situations we use the following strategy:
first the influences of the and- and balanced lists are calculated separately and the
highest among them label is assigned to the higher level goal.
Apart from the refinement links discussed so far, we can also define conflicts,
which represent negative relations between goals or lists of goals.
conflicts_with: AND_GOAL_LIST × AND_GOAL_LIST: Represents joint negative effect
between lists of goals – the goals in both lists cannot be satisfi(c)ed or weakly
satisficed at the same time. More precisely, if all goals in one list are satisfi(c)ed then
at least one goal in the other is failed or denied; if all goals in one list are at least
weakly satisficed, at least one goal in the other is at most weakly denied.
weakly_conflicts_with: AND_GOAL_LIST × AND_GOAL_LIST: Represents weak joint negative
effect between lists of goals, i.e., the goals in both lists cannot be satisfi(c)ed at the
same time. More precisely, if all goals in one list are satisfi(c)ed then at least one goal
satisfices link
contribution relation
weak conflict link
soft goal
++ ++
G7.1 G7.2 G7.3
in the other is at most weakly denied; if all goals in one list are at least weakly
satisficed then at least one goal in the other is at most weakly satisficed.
Conflicts can be defined at each two levels of the goal hierarchy, however if the
hierarchy is sufficiently complete then these conflicts should be propagated through
the goal refinement to the lowest level at which the sources of the conflicts can be
found. Conflicts can also be used at the analysis and evaluation phases for
propagating satisfaction labels when only partial information is available. For
example let goals
g1 and g2 be in conflict at the lowest level of the goals structure and
g1 is known to be satisfied. Then if the satisfaction label of g2 is not known it can be
assumed at most weakly denied if g2 is soft and failed if g2 is a hard goal. If however
it is known that g2 is satisfi(c)ed then there is an inconsistency in the specification. In
the example on Fig.1 goals
G8 “It is required to minimize training for personnel” and G9 “It is
required to minimize recruitment of personnel”
are in weak conflict with G7.
4 Goal-based Evaluation of Performance
Every task performed in an organization contributes to the satisfaction of a certain
organizational goal(s). Tasks are realized by processes in the organization’s
workflow. Each goal is formed based on a PI(s). This PI(s) can be measured (directly
or indirectly) during or after the process execution depending on the goal evaluation
type, in the end or during a certain period of time (an evaluation period defined as a
goal horizon). Then, by comparing the measured value(s) with the goal expression(s),
the satisfaction (degree of satisficing) of the goal(s) is determined. In many cases
however it is not feasible (e.g., too expensive or difficult) or not possible to monitor
and measure all necessary PIs. Then the mechanisms for propagating goal satisfaction
values through the goals hierarchy can be used. It is only necessary to evaluate the PIs
of the low-level goals. The obtained goal satisfaction values are propagated using the
rules from Section 3, upwards in the goal hierarchy for determining the satisfaction
(degree of satisficing) of higher-level goals. Thus, the organizational performance is
evaluated by determining the satisfaction (degree of satisficing) of key organizational
goals. The same principles can be applied for evaluation of agent performance.
As illustration of the proposed performance evaluation procedure consider the
following example. For estimating the performance of the organization introduced in
Section 1, the degree of satisficing of the high-level soft goal
G3 (defined in Section 2)
has to be determined.
G3 is refined into an and-list of more specific goals including
hard goal
G3.1 (Section 2) to restrict the duration of the short-term planning process
and other goals on efficiency of forward planning, plans distribution, realization of
plans, etc.
G3.1 is linked to G3 by a satisfices-link, therefore satisfices(G3.1, G3), G3.1 in
turn is refined into an and-list consisting of two hard goals:
G3.1.1 “It is required to achieve
that within 48 hours from receiving a new contract, a new short-term plan is produced”
and G3.1.2 “It is
required to achieve that within 48 hours from receiving data about necessary changes in the short-term plan,
an updated short-term plan is produced”
. The PIs corresponding to G3.1.1 and G3.1.2 are
PI1:“time needed to create a short term plan” and PI2:“time needed to update a short term plan”. These
lower-level goals are related to tasks specified in the task graph:
G3.1.1 is related to the
task T1:“generate a new short-term plan” and G3.1.2 to the task T2:“update short term plan”.
By measuring the actual task execution during the evaluation period defined for
G3.1 (a month), it is determined that values for both PIs corresponding to G3.1.1 and
G3.1.2 (PI1 and PI2) do not exceed 48 hours. Therefore the hard goals G3.1.1 and G3.1.2
are satisfied. Due to the refinement relations, it can be concluded that G3.1 is also
satisfied, therefore it contributes maximally to the satisfaction of G3 – if for example
all other goals in the refinement are satisfi(c)ed then
G3 is considered satisficed. G3 is
at the highest level of the goals hierarchy, thus its satisfaction gives a strong positive
evaluation of the overall organizational performance.
5 Methodological Issues of Goal Design
Usually, high level goals of a company are of a strategic (long-term) type. Such goals
are often made operational by refining them into lower-level tactical (short-term)
goals (a top-down approach). The refinement of goals may proceed until subgoals are
found, which could be realized by lowest-level tasks from the task hierarchy. In
practice, the top-down design approach is often combined with the bottom-up
approach, performed by aggregation of goals. In the goal elicitation approach
described in [4] subgoals are identified by asking “how” questions about the goals
already defined, and parent goals are identified by asking “why” questions. Many of
the strategic and tactical goals can be extracted from organizational documents (e.g.,
organizational strategy, mission statements, policies). Although, goals and objectives
may not be stated very precisely in these documents, still in most cases PI expressions
can be extracted from them. Further, the obtained PI expressions are used to formulate
formal goals. Relations between goals can be identified via relations in the PI
structures [6]. To ensure consistency of goal and PI structures, consistency checks can
be performed as follows. If goals are related by refinement relation, then the PIs
corresponding to these goals are related by causality relation. If the PI expressions for
goals related by refinement, contain comparison functions (i.e., ‘
>’,’<’) or measures of
degrees (i.e., ‘high’, ‘low’), or goal patterns are specified by change functions (i.e.,
increased’, ‘decreased’), then the specific type of causality may be determined. For
example, the goal
“It is required to limit the duration of the reviewing process to a month” (with PI
“duration of reviewing process < 1month”) has a subgoal: “It is desired to increase the
number of reviewers”
(goal pattern “increase(number of reviewers)”). Since goals are related by
refinement, the PIs
“number of reviewers” and “duration of reviewing process” should be related
by negative causality relation (e.g., increasing the first PI decreases the second PI).
The identification of conflict relations between goals is of importance for the
design and evaluation of organizations. To identify conflicts, the goal patterns and the
PIs structure can be used: by knowing the type of a causality relation between PIs and
the types of goal patterns, the presence of a conflict between goals can be determined.
For example, the goal
“It is required to maximize the time spent on examining a plan proposal for
and the goal “It is required to minimize the time spent on producing a correct plan” are in
conflict, since the PIs
“the time spent on examining a plan proposal for correctness” and “the time
spent on producing a correct plan”
are related by positive causality relation, and the
corresponding goal patterns are based on opposite functions:
maximize and minimize. If
needed, consistency can be achieved applying conflict resolution techniques [9, 8].
6 Discussion
Goal-oriented modeling is given a special place in the area of enterprise engineering.
Some aspects of our definition of a goal are inspired by state-of-the-art approaches in
enterprise modeling and requirement engineering. There are however significant
differences. For the first time in this framework the concept of a goal is defined based
on PIs, to reflect the way the notions are used in practice and provide intuitive
mechanisms for performance evaluation. Though large body of research on PIs exists
in management science, PIs are hardly ever considered in enterprise modeling [6].
In the enterprise modeling framework CIMOSA [2] the notion of objective is used
to represent business goals for a domain of the enterprise. Unlike this framework, no
distinction is made between hard and soft goals. Relations between the objectives are
not defined and no hierarchy of objectives is built. Also in the TOVE framework [2]
hard and soft goals are not differentiated. Goals can be decomposed in AND/OR
subgoal trees. Goals are treated very simply in the Aris enterprise architecture [2]: no
distinction between soft and hard goals is made, also refinement of goals is not
elaborated. The methodology GRAI/GIM [2] models performance indicators in the
context of decision making, without taking into account relationships with goals.
The i* approach [11] focuses on the dependencies relationships between the
actors. The approach models hard and soft goals and goal dependency relationships
between actors w.r.t. goals. In contrast to our approach, goals in i* are specified
informally, no unified representation is enforced and no relation to PIs is established.
The goal and task hierarchies are coupled in i*, tasks are decomposed to goals and
tasks. In the framework proposed, models of tasks and goals can be specified and
analyzed separately. Furthermore, relations between goals and tasks can be
established and used in analysis in the proposed framework. Tropos [1] is a
methodology for agent-oriented software development based on i* and goals are
treated similarly. The KAOS methodology [4] focuses on requirements elaboration
and provides support in connecting high-level goals to operations, objects and
constraints to be implemented by the software. A goal is defined as an objective to be
achieved by the system; an operational objective is called a constraint. Soft goals are
not considered. Goals and constraints are defined formally using the patterns achieve,
cease, maintain, avoid, optimize, which are reused in our approach in the notion of a
goal pattern. A difference is that in our approach a goal pattern establishes a direct
relation to a PI expression. KAOS goals are formalized using temporal logic and
structured and operationalized to constraints in AND/OR graphs. In our approach a
wider set of goals is considered, some of which cannot be expressed as temporal logic
formulae. The NFR framework [5] focuses on representing non-functional
requirements on the software system as interrelated goals. Three types of goals are
defined: NFR, satisficing and argumentation goals. The last two model design
decisions and arguments. The NFR goals are soft goals which can be refined using
different types of relationships describing how the satisficing of the offspring relates
to the satisficing of the parent goal. The label propagation procedure in our approach
is inspired by but different from the one of the NFR framework. We consider only
positive refinement links; negative links are modeled by conflict links. Besides, we
enrich the refinement with a new balanced type of relation for soft goals, not
previously used in literature, for finer definition of joint contributions of sets of goals
to the satisficing of higher-level goals. Furthermore, in contrast to NFR we relate
goals to PIs and other concepts (e.g., tasks, roles, agents) explicitly, which enables
different types of analysis across different views on organizations.
Agent goals in multi-agent systems are specified by declarative logical
specifications that describe states of the agent system, which are desirable and could
be realized by the agent. Only hard goals are considered. Such declarative goals often
have a simple (software-oriented) format and are often operationalized in agent
programming languages by sequences of actions or plans [10]. Then, the distinction
between goals and tasks, essential for our framework, is not tangible any more.
In summary, this paper presents a formal goal-oriented modeling approach in the
context of the performance-oriented view on organizations. The proposed approach is
based on the idea that goals should be defined over organizational performance
indicators. The proposed approach includes a diverse vocabulary to express goal-
related concepts and relations, in particular w.r.t. performance evaluation, e.g.,
organizational or individual, hard or soft goals, how they contribute to or conflict with
each other’s satisfaction, mechanisms for identifying the (level of) satisfaction/
satisficing of goals, as well as guidelines and techniques for building consistent goal
structures. Goals are related to concepts described in other views of the framework as
well, which enables different types of analysis within and between views; they are
mentioned here but will be elaborated and applied on larger case studies elsewhere.
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