=Paper=
{{Paper
|id=Vol-3896/paper7
|storemode=property
|title=Algorithm for conceptual problem-solving using General Environment Description Language
|pdfUrl=https://ceur-ws.org/Vol-3896/paper7.pdf
|volume=Vol-3896
|authors=Nina Bąkowska,Krzysztof Zatwarnicki
|dblpUrl=https://dblp.org/rec/conf/ittap/BakowskaZ24
}}
==Algorithm for conceptual problem-solving using General Environment Description Language==
https://ceur-ws.org/Vol-3896/paper7.pdf
Algorithm for conceptual problem-solving
using General
Environment Description Language
Nina Bąkowska1,*,† and Krzysztof Zatwarnicki1,†
1
Opole University of Technology, Proszkowska 76, 45-758 Opole, Poland
Abstract
The development of automated systems has been demonstrated to be a significant area of study, with
rapid advancement occurring with the advent of artificial intelligence. In order to provide a universal
method of problem-solving, an algorithm for executing various tasks within environments described
with the General Environment Description Language (GEDL) has been developed. The initial version
of the algorithm, which operated at a conceptual level, was confronted with considerable challenges,
including inefficiency in the utilization of resources, the potential for infinite loops, and the difficulty
of applying conceptual solutions to real-world scenarios. To address these issues, several key
enhancements were introduced. These included the implementation of a state queue to prevent
repetitive state exploration and optimization in the management of instances, features, and
relationships. The enhanced algorithm was evaluated using a practical case study involving a
washing machine, wherein it effectively circumvented infinite loops and markedly reduced execution
time. Additionally, the algorithm was capable of identifying absent instances and relationships. The
outcomes illustrate that the modifications not only enhance the algorithm's performance but also
expand its applicability to more complex and diverse environments. Future research will concentrate
on integrating evolutionary algorithms to further optimize solutions and address challenges related
to missing instances and implicit parameters in the problem-solving process.
Keywords
automated systems, algorithm, algorithm optimization, environment description languages,
problem solving, knowledge representation, problem-solving 1
1.Introduction
The development of automated systems capable of performing tasks without human
supervision has witnessed considerable advancement in recent years. From industrial robots to
intelligent personal assistants, these systems have had a profound impact on numerous facets of
daily life and industry [1]. This drive towards automation can be seen as a fundamental aspect of
human nature, namely the tendency to solve problems. This has been a significant factor in the
advancement of technology over time.
ITTAP’2024: 4th International Workshop on Information Technologies: Theoretical and Applied Problems,
November 20–22, 2024, Ternopil, Ukraine, Opole, Poland ∗ Corresponding author.
†
These authors contributed equally.
bakowskanina1704@gmail.com (N. Bąkowska); k.zatwarnicki@po.opole.pl (K. Zatwarnicki); 0009-0000-7719-
2379 (N. Bąkowska); 0000-0001-6156-6030 (K. Zatwarnicki)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
The introduction of artificial intelligence (AI) marked a significant turning point in the
evolution of automated systems. Artificial intelligence has developed rapidly, offering
sophisticated solutions to complex problems across a wide range of domains. Nevertheless,
while AI displays considerable proficiency in certain domains, there is a clear requirement for
more adaptable systems that can accommodate a diverse range of environments and challenges,
rather than being constrained by a narrow focus on a single application.
In response to this need, our previous work [2] has concentrated on the creation of the
General Environment Description Language (GEDL) [3] and an accompanying algorithm
designed to resolve issues in environments described by GEDL. The objective of this approach
was to provide a more general solution framework that operates at a conceptual level. The
algorithm was structured in a manner that enabled navigation through a series of abstract
actions and states, thereby effectively solving problems in a simulated, idealised environment.
Although this conceptual approach proved valuable in generating solutions that could
theoretically achieve the desired goals, it became evident that the algorithm encountered
difficulties when applied to real-world scenarios.
The fundamental challenge lies in the fact that the algorithm, by design, operates primarily
at the conceptual level, where the inherent complexity of real-world instances— such as varying
parameters, unpredictable conditions, and multiple potential instances of objects—is not fully
accounted for. The conceptual solutions generated by the algorithm do not automatically
translate into executable actions within a real environment. This is due to the necessity of
considering physical constraints, resource availability and the precise state of objects, which are
absent from the conceptual level. Consequently, while the algorithm effectively delineates a
potential sequence of actions in an idealised environment, it frequently fails to address the
practical challenges encountered when those actions are mapped onto real-world occurrences.
This discrepancy between conceptual problem-solving and practical execution demonstrates
the shortcomings of the original algorithm. It is crucial to overcome the gap between abstract
planning and its real-world implementation. It needs to be ensured that solutions are not only
theoretically sound but also executable in the actual environments in which they are intended
to operate.
2.Related work
This research is aligned with the broader goals of artificial general intelligence (AGI) [4],
which aims to create intelligent systems capable of performing any intellectual task that a
human can undertake. The ability to solve problems is a fundamental aspect of human
cognition. It involves the organisation and structuring of knowledge in a flexible and adaptive
manner, which is a key area of focus in the development of AGI. Research in this field has
contributed to the theoretical foundations of AGI by exploring how human-like general
intelligence can be structured within AI architectures. This emphasises the importance of
knowledge management and reasoning.
A number of description languages have been developed with the objective of formalising
and organising a variety of tasks and domains within artificial environments. Such languages
include Game Description Languages (GDL) [5], which are used to describe the rules and
dynamics of different games, and Video Game Description Languages (VGDL) [6], which extend
this concept to video games. Although both GDL and VGDL have proven useful in specific
contexts, their primary drawback is their lack of universality, as they are designed exclusively
for use with games. However, languages such as GDL-III [7] have introduced the ability to
model more complex scenarios by incorporating epistemic logic, which accounts for agents'
knowledge within the game environment.
In contrast, agent-based description languages, such as the JIAC Agent Description
Language (JADL) [8] and the Java Agent Development Framework Language (JADEL) [9], are
more versatile in their scope, focusing on defining the characteristics and behaviours of agents
within multi-agent environments. These languages provide frameworks for modelling
interactions between multiple agents, thereby enabling the design of more sophisticated
simulations that extend beyond rigid rule-based systems. Moreover, the research on
compositional languages in multi-agent populations introduces a grounded approach, whereby
agents develop their language to coordinate actions and behaviours within an environment.
Our work is based on the General Environment Description Language (GEDL), which
provides an even broader framework for representing complex environments and interactions.
In contrast to the aforementioned languages, GEDL offers a wide range of possibilities for
describing dynamic and diverse environments. The flexibility of GEDL makes it particularly
suitable for this research, where complex problem-solving requires a detailed representation of
both the environment and the relationships between its entities.
A systematic approach to provide a reliable solution has resulted in the development of an
initial algorithm designed to solve problems within environments defined by the GEDL. This
earlier approach established the foundation for the derivation of fundamental conceptual
solutions within a simulated environment. However, it also revealed significant challenges,
including inefficiencies in resource management, the potential for infinite loops, and the
difficulty of applying conceptual solutions to real-world occurrences. These insights have
informed the development of the enhanced algorithm presented in this paper, which addresses
these limitations and extends the applicability of GEDL to more complex and realistic
environments.
In recent years, there has been a notable increase in interest in the areas of task execution
and problem-solving within the context of specific simulated environments, such as the video
game Minecraft. The frameworks MCU [10] and JARVIS-1 [11] represent significant advances in
the evaluation of multi-task agents and agent-based control in openworld environments. To
illustrate, MCU introduces a task-centric framework designed to evaluate agent performance
across a wide variety of tasks, employing a set of difficulty metrics to assess an agent's
capabilities. Similarly, JARVIS-1 employs multi-modal language models and memory-
augmented systems to facilitate the completion of complex tasks within the open-ended
Minecraft environment. These systems offer valuable insights into problem-solving and task
planning in environments that exhibit a high degree of complexity and variability.
Another notable advancement in AGI development is evidenced by ChatGPT [12], which is
capable of generating step-by-step problem-solving solutions. However, ChatGPT's reliance on
natural language processing and statistical models limits its reliability in complex environments
that require precise control over the problem-solving process. By leveraging GEDL, our work
offers a more structured control over the environment and greater transparency into the steps
required to solve problems in dynamic, multi-agent settings.
2.1. Motivation
The objective of this work is to present an enhanced approach that addresses the issues
previously identified, thereby improving the reliability and efficiency of problem-solving in
complex environments. It is hoped that, by confronting a previously developed approach with
an example that highlights its shortcomings, the importance of readjusting the solution to cover
a wider range of problems will become clear. This marks a major advance in enhancing the
effectiveness of existing algorithm and opening up new possibilities for their use. The objective
of this paper is to provide a more robust and flexible solution that can handle the details of
complex environments, leading to the acquisition of more reliable and efficient outcomes.
3.General Environment Description Language
The General Environment Description Language is a framework designed to provide a
description of the environment in which robots or agents operate. This enables the assignment
of meaning to objects and the planning of tasks based on those objects. The GEDL is inspired by
the manner in which the human mind organises and processes knowledge. The human mind
constructs a conceptual system, a structured set of ideas and thoughts, and learns from
mistakes, thereby refining this system over time. Furthermore, humans are capable of passing
entire conceptual frameworks to others, thereby reducing the time and resources that would
otherwise be expended on individual learning.
In GEDL, the term "environment" is used to represent a physical or conceptual reality,
comprising individuals and objects. The acquisition of knowledge is achieved through the
cognitive mechanisms that enable individuals to recognise objects as instances with specific
features. These features are grouped into sets that are associated with particular instances. The
relationships between objects or individuals are based on logical connections derived from the
aforementioned features. Furthermore, the language defines the concept of "relationship" which
categorises these connections.
In GEDL, instances are capable of performing actions, either independently or with
assistance, with the objective of modifying the environment. Actions modify the state of the
environment by altering the features, relationships, or even the existence of objects. Each action
is regarded as an indivisible unit; for instance, the placement of an object on a shelf is treated as
a single, complete action, rather than a series of smaller movements.
A problem in GEDL is defined as the task of modifying the environment from its initial state
to a desired final state, utilising the available actions. In some cases, multiple solutions may
exist, with the optimal one depending on the desired outcome, such as the minimisation of time
or steps.
The formation of an individual's understanding of reality is a function of accumulated
knowledge, which is referred to as "individual knowledge." This includes information collected
from the environment and concepts learned. This knowledge can be developed through
observation, deduction, and the exchange of information with others, the latter of which has
been demonstrated to be an effective method for accelerating learning. In GEDL, individual
knowledge is comprised of three elements:
• Conceptual System - represents the individual's perception of entities, relationships, and
activities. This represents the individual's perception of entities, relationships, and
activities. It encompasses features, feature sets, instance concepts, relationship concepts,
and action concepts. A feature is defined by a name and a range of possible values, which
allows instances to be classified. The grouping of attributes that are common to objects of
the same instance constitutes a feature set, thereby facilitating the process of classification.
Relationship concepts define the connections between instances, which may be subject to
alteration through the performance of actions.
• Occurrences - these are particular instances and relationships that are identified by the
individual. In contrast to the broader Conceptual System, Occurrences are concrete entities,
characterised by defined features and values that can be acted upon.
• Experience - this aspect of knowledge provides the impetus for the search for solutions to
problems. By defining a problem in terms of the current and desired state of the
environment, the individual employs their experiences to identify pertinent actions and
construct a step-by-step plan to achieve the desired outcome.
GEDL offers a systematic methodology for describing environments, comprehending the
relationships between objects, and planning effective actions to address problems. It draws
heavily upon the principles of human cognition, including how we learn, organize, and share
knowledge.
3.1. Example
In order to more clearly demonstrate the potential of GEDL notation, a familiar, everyday
task – namely, the washing of clothes – was described using this notation. Although the act of
laundering clothing may appear to be a relatively simple and straightforward process, it is, in
fact, comprised of a series of complex steps and requires the fulfilment of several prerequisites
before any action can be initiated. The environment is defined using JSON syntax. This allows
the description to be both universal and easily understood by humans.
In this scenario, the Conceptual System comprises several key concepts of components,
including a manipulator, a washing machine, a laundry basket, and the laundry itself. Each of
these components is represented as a distinct instance concept within the GEDL framework,
complete with specific features and relationships pertinent to the task at hand. To illustrate, the
washing machine concept includes features such as 'turned on', 'contains laundry', 'contains
detergent', 'washing', 'selected washing program', 'access inside' and 'access drawer'. These
features are directly related to the washing machine's functions; irrelevant attributes such as
size, power consumption and water usage are omitted since they do not impact the task at hand.
A comparable level of detail is applied to the remaining components. The manipulator is
assigned a feature indicating whether it is holding an object, the basket is assigned a feature
indicating whether it contains laundry, and the laundry is assigned a feature indicating whether
it is clean. Furthermore, two relationship concepts, "holds" and "contains," are defined to specify
the interactions between these objects, such as the manipulator holding an item or the basket
containing laundry. The simplified example illustrated in Figure 1 provides a helpful overview
of this concept. It should be acknowledged that this illustration do not provide a comprehensive
account of the details in question; rather, its purpose is to assist the reader in understanding the
manner in which the Conceptual System is represented.
Figure 1 Simplified illustration of the doing laundry example
To represent the entire process of doing laundry, twelve distinct action concepts are defined.
These include the following: 'takeOutOfBasket', 'openDoor', 'closeDoor',
'putIntoWashingMachine', 'openDrawer', 'closeDrawer', 'addDetergent',
'turnOnWashingMachine', 'chooseProgram', 'pressStart', 'takeOutOfWashingMachine', and
'putInBasket'. It should be noted that the sequence in which the actions are performed does not
necessarily correspond with that of a one-to-one set of steps. In some instances, the
performance of an action may necessitate the repetition of a previous action in order to
complete the task; this reflects the iterative nature of numerous activities that occur on a daily
basis.
Each action is associated with specific objects in the environment and is subject to certain
conditions. To illustrate, the action of placing laundry into the washing machine necessitates
the presence of three objects in designated states: the manipulator must be in possession of dirty
laundry, and the washing machine must be in an open and unloaded configuration. Once the
action has been completed, the manipulator is no longer holding any items and the laundry is
inside the washing machine. However, the door remains open since closing it is treated as a
separate atomic action.
The Occurrences section delineates the initial state of the environment at the inception of the
laundry task. In this initial state, the manipulator is not holding any items, the basket contains
dirty laundry, and the washing machine is turned off, empty, and without detergent, with no
access inside. This part mirrors the state of actual, existing instances.
The problem, as defined in the Experience section, is to successfully complete the laundry
task using the instances defined in the individual's knowledge, which include the manipulator,
washing machine, basket, and laundry. The ultimate goal is to obtain clean clothes. The only
difference between the initial and final state is the value of the 'clean' feature. However,
achieving this goal is not a matter of performing a single action; rather, it requires a series of
modifications to the instances described in Occurrences.
4. Initial solution
The primary objective of the algorithm remains unchanged across all existing versions. It is
to determine a path from an initial state to a final state by methodically exploring all possible
actions that can be executed in a given environment. The approach involves a systematic
exploration of possible state transitions to ascertain whether a solution exists and to outline the
steps required to achieve the desired final state.
Initially, the algorithm takes the given initial state and evaluates the set of possible actions
that can be performed within that state. Each feasible action leads to the creation of a new state,
which is then subjected to a similar evaluation process. This iterative process continues, with
the algorithm exploring each newly generated state by determining the subsequent actions that
can be performed.
The process begins with the algorithm analyzing the prevailing environmental
circumstances, taking into account initial positions, features, conditions and any existing
relationships between objects. The data is then employed in the construction of a simulated
scenario, which is utilized as a means of facilitating the process of problem-solving.
After that, the algorithm identifies and executes a feasible action based on the current state.
This approach enables the determination of the specific (on the conceptual level) instance that
performs the action at any given time. Once the action has been selected, it is executed, resulting
in alterations to the environment. These may include the movement of objects, the modification
of their properties, or the creation of new relationships.
Subsequently, the algorithm determines whether the objective has been reached. If this is the
case, the sequence of actions is identified as a potential solution. Conversely, if the objective has
not been met, the algorithm commences the process anew from the updated environmental
state, searching for and executing the next available action. The algorithm's execution
terminates under two conditions: if the final state is achieved, indicating that the problem is
solved, or if there are no more states left to explore, signifying that no solution exists for the
given problem. The illustration of this process is presented in the Figure 2.
Figure 2: Illustrated process of finding a solution
4.1. Challenging example
The task of doing laundry can be used as a practical example to illustrate the challenges
encountered in early stages of algorithmic design. The conceptual problem is straightforward:
the initial state features dirty laundry, and the final goal is to have clean laundry. In order to
achieve this goal, a series of actions must be carried out. These include removing the laundry
from the basket, opening the washing machine door, placing the laundry inside, closing the
door, adding detergent, starting the machine, waiting for the wash cycle to complete, removing
the laundry, and finally, placing it back in the basket.
Although the conceptual solution may appear straightforward, several issues emerge when
considering the possibility of multiple outcomes or translating it into a practical algorithm that
operates within a real environment. For the sake of this example, we may consider the following
scenario: the environment consists of multiple instances, including two washing machines (one
functional and one broken), two baskets (one with laundry and one empty), a piece of dirty
laundry, and a manipulator (a mechanical arm) that can interact with these objects.
4.1.1.Time and Resource Consumption
A significant challenge is the vast number of potential outcomes that must be taken into
account at each stage of the process. In any given state, the manipulator is capable of
performing a range of actions, including opening doors, moving laundry, and adding detergent,
among others. Each of these actions results in a new state, and all potential outcomes must be
considered to guarantee that the algorithm does not overlook a viable path to the goal. The
combinatorial explosion of potential actions and states renders the algorithm highly resource-
intensive, as it must explore a multitude of potential sequences of actions. The necessity to
assess each of these possibilities results in a considerable investment of time and resources,
which has the potential to significantly impair the efficiency of the algorithm.
4.1.2.Looping
A further significant issue is that of infinite loops, which is particularly evident in the context
of the laundry example. The environment permits actions that are cyclical in nature, such as
repeatedly opening and closing the washing machine door or the detergent drawer. Moreover,
actions such as placing laundry into the washing machine, closing the door, opening the door,
removing the laundry, and then repeating the process could result in an infinite loop. Such
cycles are not conducive to achieving the desired outcome; rather, they result in the inefficient
utilization of computational resources and time. Without careful management, the likelihood of
the algorithm becoming trapped in these futile loops is high. This would effectively halt its
ability to progress towards the final state.
4.1.3.Real World Application
A final and equally crucial challenge is to ascertain whether the conceptual solution can be
implemented in the actual environment. To illustrate, the conceptual solution is based on the
assumption that a functional washing machine is available. However, should the only available
washing machine be in a state of malfunction, the entire plan becomes unworkable,
necessitating the rejection of the algorithm and the pursuit of an alternative solution. The
verification process is inherently time-consuming due to the necessity of evaluating the
compatibility of the conceptual solution with the actual objects present in the environment
(Occurrences). The algorithm may require multiple iterations through potential solutions,
discarding those that are infeasible, before identifying a viable option. This iterative process of
generating and verifying solutions introduces an additional layer of complexity and time
consumption to the algorithm.
It is imperative that these challenges be addressed in order to create an efficient algorithm.
5.Improved algorithm description
To address the challenges mentioned earlier, an improved version of the algorithm was
developed to tackle more complex problems defined using the General Environment
Description Language (GEDL). In the initial version of the algorithm, three critical issues were
identified: resource inefficiency, the risk of infinite loops, and difficulties in real-life application.
The improved version specifically focused on resolving the first two issues, which were crucial
for enhancing the algorithm's performance and reliability.
The problem of resource inefficiency arose from the large number of operations and loops
that the algorithm executed during the exploration process. As the algorithm evaluated possible
actions and resulting states, the sheer volume of these operations led to significantly prolonged
execution times, making the process less efficient and scalable for complex environments.
Additionally, the potential for infinite loops became apparent when the algorithm repeatedly
cycled through certain states without making meaningful progress toward the goal. For
example, the algorithm might get stuck in a loop where it continuously opens and closes a door,
failing to advance toward the final state. This issue not only wasted computational resources but
also hindered the algorithm's ability to find a viable solution.
To overcome these challenges, several key enhancements were proposed and implemented.
One of the most significant improvements was the introduction of a state queue. This queue acts
as a central mechanism for managing the states encountered during the simulation. The
algorithm now begins by taking the first state from the queue and appending all possible
resultant states that can be achieved from this initial state. Before adding any new state to the
queue, the algorithm checks whether this state has already been encountered in previous
iterations. If the state has been encountered before, it is discarded to prevent the algorithm from
entering an infinite loop. On the other hand, if the state is new, it is added to the queue, allowing
the exploration process to continue. This approach effectively eliminates the risk of repetitive
cycles and ensures that the algorithm makes continuous progress toward the final goal.
In addition to addressing infinite loops, further optimizations were implemented to reduce
resource consumption. One significant optimization involved the use of references instead of
duplicating instances, such as a washing machine, at each stage of the process. In the initial
version, each state transition often involved creating cloned and slightly modified copies of the
environment, which contributed to the resource inefficiency. By utilizing references, the
algorithm can now directly address the initial state and the set of modifications made to reach
the current state, rather than creating multiple copies. This approach substantially reduces the
overhead associated with state transitions and minimizes the number of environmental states
that need to be considered, thereby enhancing the overall efficiency of the algorithm.
With regard to the third issue, namely the inability to verify the applicability of a conceptual
solution in real-world scenarios, the improved algorithm incorporates a
mechanism that systematically checks for the presence of required instances, features, and
relationships in real-world occurrences subsequent to the generation of a conceptual solution.
The process commences with the algorithm formulating a step-by-step solution based on the
concepts delineated within the GEDL framework. Once a conceptual solution has been
established, the algorithm proceeds to verify its feasibility in a real-world environment. In
particular, the solution is cross-referenced with the available instances, thus ensuring that the
requisite features and relationships are present in the occurrences.
In the event that the required instances or relationships are absent, the algorithm identifies
and presents a list of these missing assets, indicating that the proposed solution is not viable in
the current environment. This feature enables a more informed evaluation of the solution's
practicality, identifying the gaps that must be addressed for successful implementation.
Conversely, if all the requisite instances and relationships are present, the step-by-step solution
is deemed potentially executable, indicating a higher probability of success in a real-world
context.
This enhancement not only enhances the algorithm's ability to solve problems but also
provides a diagnostic tool that can guide users in adapting or refining their solutions based on
the actual resources and constraints of the environment. The updated version of the algorithm
is presented in Figure 3.
Figure 3: Updated process of finding a solution
6.Results
The enhanced algorithm was evaluated in a scenario that involved a washing machine,
laundry, a basket, and a manipulator. This scenario was designed to assess the algorithm's
capacity to effectively address complex problems within dynamic environments. This example
demonstrated the significant advancements achieved by the algorithm.
Firstly, the implementation of the state queue effectively resolved the issue of infinite loops,
such as those caused by repeatedly opening and closing doors or drawers. In earlier versions of
the algorithm, these actions could result in an infinite loop, necessitating the introduction of
artificial constraints to prevent such behaviour. However, the introduction of the queue has
enabled the algorithm to rapidly identify and discard repetitive states. The initial iteration of
any loop is promptly identified, thus ensuring that the algorithm does not expend resources on
redundant actions. This improvement has not only maintained the algorithm's universality but
has also considerably reduced the execution time.
Furthermore, the intelligent administration of components, including instances, features,
and relationships, has resulted in notable enhancements in processing speed. The algorithm is
now able to propose a conceptual solution with greater rapidity than was previously possible,
due to its more efficient handling of these elements. In the washing machine scenario, the initial
conceptual solution proposed by the algorithm was accurate and executable in real-world
contexts, thereby demonstrating the efficacy of the enhancements.
Furthermore, the algorithm's flexibility was demonstrated through a test in which the basket
containing laundry was replaced with an empty one. The queue mechanism promptly identified
that the required actions could not be executed, resulting in the prompt depletion of the queue.
This resulted in the identification of missing elements, specifically the laundry and the
relationship "contains" (as in "basket contains laundry"). The algorithm's capacity to detect and
report such deficiencies highlights its enhanced utility in navigating diverse environmental
setups.
In conclusion, the modifications introduced have enhanced the algorithm's adaptability and
efficiency, allowing it to operate in a broader range of environments while maintaining robust
performance in complex, dynamic scenarios.
7.Direction of future work
In subsequent research, further challenges will be investigated with a view to further
refining this approach. A crucial consideration is the manner in which the algorithm should
respond when a necessary instance within the conceptual system is absent in the real-world
occurrences. This scenario gives rise to a number of important questions. The question thus
arises as to whether the algorithm should attempt to solve the problem once more, this time
without relying on concepts that require the use of unavailable instances. Furthermore, if it is
feasible to obtain or create the missing instance, should additional steps be incorporated into the
solution to account for its production? The resolution of these questions will be crucial to the
development of a more adaptable and intelligent problem-solving system.
To enhance the algorithm's efficiency, future research will also focus on refining the search
space through the utilization of Individual Knowledge Fragments (IKFs). By specifying the
instances available in the environment, IKFs can assist in narrowing down the search, thereby
reducing the computational burden by limiting the algorithm's focus to the relevant concepts
and instances. This approach will guarantee that the algorithm exclusively contemplates
solutions that are based on the actual resources that are currently available, thereby enhancing
both the speed and the accuracy of the process.
A further significant challenge is posed by the algorithm's capacity to identify implicit
parameters essential for problem-solving, particularly in instances where such parameters are
not explicitly specified. To illustrate, if the task is to cut a hole in a wooden plank of a specific
size to fit a given ball, a human being can deduce that the diameter of the hole should be the
same as that of the sphere. However, this kind of logical inference is not a straightforward
process for an algorithm. Future work will investigate methods of equipping the algorithm with
the capability to derive such implicit parameters, potentially through the integration of domain-
specific knowledge or heuristic reasoning techniques. This will facilitate the algorithm's ability
to process a more expansive range of tasks where not all requisite details are explicitly defined,
thereby enhancing its versatility and problemsolving capabilities.
Further work will be carried out to improve the algorithm's capacity to process missing
instances or relationships when a conceptual solution is not viable in real-world scenarios. It is
possible to enhance the presented approach, particularly in the context of identifying solutions
within occurrences. In order to address this challenge, it is proposed that evolutionary
algorithms be integrated into the problem-solving framework. Evolutionary algorithms, which
are inspired by the process of natural selection, offer a powerful method for optimizing
solutions in complex environments. In this context, the sequence of actions (steps) can be
represented as a genome, with each gene corresponding to a specific action or decision. It is
anticipated that the incorporation of evolutionary algorithms will result in a number of notable
benefits, including the capacity to adapt to complex environments containing multiple
instances and varying constraints. The algorithm would be better equipped to handle these
environments by evolving strategies in an adaptive manner, with each strategy being tailored to
the specific characteristics of the environment in question. Over successive generations, the
evolutionary algorithm would progressively enhance its solutions, learning from past successes
and failures. It seems plausible to suggest that the iterative refinement process is likely to result
in the generation of more reliable solutions.
8.Summary
The objective of this work was to develop and refine an algorithm capable of solving
problems within environments described by the General Environment Description Language
(GEDL). The initial version of the algorithm was effective at the conceptual level, generating
solutions through the exploration of potential actions and state transitions. However, several
significant obstacles were identified that restricted its applicability in real-world scenarios.
These challenges included inefficiency in the use of resources, the risk of infinite loops, and
difficulties in translating conceptual solutions into practical actions within real-world
occurrences.
In order to surmount these limitations, a number of pivotal enhancements were introduced
to the algorithm. Firstly, a state queue was implemented with the objective of providing a
systematic means of managing and tracking the states encountered during the simulation. This
mechanism proved an effective means of mitigating the issue of infinite loops, ensuring that
previously encountered states were not revisited. Secondly, the consumption of resources was
markedly decreased through the utilization of references in contrast to the replication of
instances of objects at each stage of the problem-solving process. This optimization resulted in a
notable enhancement in the efficiency and scalability of the algorithm. In addition, the
algorithm was provided with the capacity to identify the absent elements, thereby reinforcing
the necessity of evaluating the viability of conceptual solutions in authentic contexts.
Despite these advancements, the algorithm still faces challenges in applying conceptual
solutions to complex real-world environments where multiple instances and varying
constraints must be considered. To address this issue, future work will concentrate on
integrating evolutionary algorithms into the problem-solving framework. Evolutionary
algorithms, which are inspired by the principles of natural selection, represent a promising
approach to optimising solutions within complex environments.
Furthermore, additional challenges will be investigated through further research. One area
of focus will be to determine whether it would be preferable to re-solve the problem or to
incorporate additional steps to create or acquire the necessary instance. A further crucial
element will be narrowing down of the environment to the pertinent concepts and instances,
thereby enhancing the algorithm's efficiency. Ultimately, efforts will be made to augment the
algorithm's capacity to infer implicit parameters essential for problem-solving, particularly in
instances where such parameters are not explicitly provided.
In conclusion, the proposed improvements and the planned future directions are designed to
develop a more robust and versatile algorithm capable of solving complex problems across a
variety of environments. By establishing a connection between conceptual planning and real-
world execution, this work contributes to the advancement of automated problem-solving
systems that are more generalisable and effective.
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