KLEP

KLEP (Key-Lock-Executable-Process) is a groundbreaking AI framework that utilizes symbolic AI for dynamic decision-making. It integrates keys, locks, executables, and processes to foster ethical, modular, and transparent AI applications, offering a novel approach for developers and researchers in AI and cognitive science.

Welcome to KLEP

Thank you for exploring KLEP (Key-Lock-Executable-Process), a pioneering approach in artificial intelligence that strides toward emulating human cognition's complexity and adaptability. KLEP signifies a paradigm shift in how machines perceive, analyze, and respond to their surroundings. It leverages a symbolic approach to AI (Artificial Intelligence), known as Good Old Fashioned AI (GOFAI), to foster a clean and dynamic flow of logic that remains transparent, unlike the often opaque mechanisms within trained black-box models (DNNs).

What Sets KLEP Apart?

KLEPis not a Deep Neural Network (DNNs) nor transformer-based systems. KLEP embodies the principles of symbolic AI, embracing a logic-driven framework that allows for the seamless integration of various AI technologies. This versatility means that if a designer chooses, KLEP can leverage the strengths of DNNs, transformer models, or any other advanced AI techniques to enhance its decision-making capabilities.

Such integration enables KLEP to utilize the depth of learning and pattern recognition offered by these systems while maintaining its symbolic logic's clarity and interpretability. It incorporates a logic system that mirrors the cognitive processes humans engage in daily. It is how I think you think – I think. KLEP's framework is based on the dynamic interplay of four primary components:

• Keys: Symbols or tokens that represent actions, permissions, or triggers.
• Locks: Conditions or states that must be met or unlocked using the appropriate keys.
• Executables: The actions or processes initiated by unlocking locks with keys.
• Processes: Sequences of actions or behaviors leading toward desired states of being.

Protected under Preliminary Patent Application 63/625,138, KLEP's methodological innovation lies in its holistic approach. KLEP's approach to information processing is far from linear or confined to predefined patterns, akin to navigating a flowchart. Instead, it embodies an adaptive problem-solving topology that considers ethical, emotional, and temporal (memory) aspects. This autonomous topology allows KLEP to dynamically adjust its logic and reasoning pathways based on a holistic view of its environment and the tasks at hand.

By integrating ethical considerations, emotional responsiveness, and the accumulation of experiences, KLEP aligns its decision-making processes closely with human reasoning. This unique convergence of logic and human-like understanding empowers KLEP to navigate complex, human-centric environments more naturally and intuitively, forging AI systems that not only adapt and learn but also relate and respond in ways that are comprehensible and meaningful to humans.

Through this innovative framework, KLEP aspires to bridge the gap between machine efficiency and human empathy, facilitating AI's integration into societal contexts with greater harmony and ethical foresight.

For Developers, Researchers, and AI Enthusiasts

Whether you're developing cutting-edge AI applications, conducting research in cognitive science, or simply fascinated by the advancements in artificial intelligence, KLEP offers a compelling framework through which to explore and innovate. Its emphasis on creating a system that models human cognitive processes promises a future where AI can operate alongside humans with greater harmony, understanding, and ethical consideration.

Welcome to a new era of artificial intelligence.

How this Project is Organized and Requirements

KLEP currently lives in a Unity3D deployment focused on video games because that is where I feel the most nimble. It is in play that we discover who we are, and therefore fitting for an approach to synthetic cognition ought to develop. It is not constrained to this platform for deployment, however, this is where it lives for the time being. The project is a stand alone project that needs no other downloads or supporting software other than the Unity3D environment.

KLEP is organized in several scenes, each building on the prior in complexity to showcase the decision making logic going on under the hood. Several observation utilities have been developed to help you see its choices and the data it is considering in real time. This proves invaluable for debugging purposes and for educational purposes.

Scene 1: Empty Neuron – This neuron lacks any knowledge and is in a state of conceptual samsara; a perpetual emptiness. This allows you to see what the system instantiates and adds to itself to function. Notice that any KlepNeuron is self contained and can instantiate all scripts that it needs to function.

Scene 2: Dynamic Knowledge Acquisition – This scene has a neuron and an executable (knowledge) living on a game object outside of the hierarchy of the neuron. By dragging that executable into the hierarchy (at any spot, not just the container that is set up for organizational purposes), that knowledge will be incorporated into the neuron and begin operation. This is to show the hot swappable nature of executables and their execution.

Scene 3: User Input – This scene allows you to inject keys into the neuron to move an agent around their game space environment; IE – you drive a little car. This shows that the neuron is able to take either frame perfect input into consideration or allow a frame + 1 operation to occur for input.

Scene 4: The Types of Executables – This scene highlights each type of executable used by the system. Sensors, which release keys based on the environment around it, Actions, which effect the world around it based on keys. Routers which convert keys or key information into other keys or key information. Goals, which manage a series of executables (remember, goals are also executables). This scene has a Tank use a sensor to sense a nearby tank (sensor), if that tank is an enemy or ally (router), then fire at that tank (action), all managed by a goal.

Scene 5: ….

API Reference

KLEP's detailed documentation is embedded directly within the codebase to facilitate ease of understanding and interaction with the system. While a formal API reference is in development, the project is accompanied by thorough tutorial scenes, each paired with descriptive text files to guide users through KLEP's functionalities and design principles. For an in-depth exploration of KLEP's theoretical foundation, practical applications, and insights into its creation, consider acquiring the companion book, available soon. The book delves deep into the conceptual underpinnings of KLEP, offering a comprehensive guide to its application and potential in shaping the future of AI.

Contribution Guidelines

Currently KLEP is a living project as shown by the 0.8 status. Meaning that while I would love support and assistance in its development, I am still a ways away from being in a position where I am certain that nothing major will change. I have been in a position where I thought it was done and that was it only to have logic raise its head and say, “Hey! What if….”

As such, if you want to support me financially, I will not stop you. A Ko-Fi has been set up to accept donations. Further, a book will ride alongside this deployment and that link will be posted when published through my Amazon bookshelf. This will be a greater examination of KLEP, the background to how it was created, development guidance and direction, thoughts of where it all goes, and much much more.

Community

As KLEP's primary developer, my capacity to engage directly with the community is currently limited. However, I am open to questions, discussions, and potential collaborations via email at Rolling4d4@gmail.com. As the project progresses towards a more stable phase, I look forward to expanding community engagement through platforms like a dedicated Discord server, facilitating a collaborative space for applying KLEP in gaming, educational projects, and beyond. Your patience and interest are greatly appreciated as we work towards making KLEP a versatile and accessible tool for AI development.

Acknowledgments

My deepest gratitude goes to everyone who has journeyed with me through the development of KLEP - from those who've endured endless discussions to skeptics who've challenged its premise. Your feedback, whether positive or critical, has been invaluable, driving me to refine and pursue this project with greater determination. This README serves as a testament to perseverance in the face of doubt and skepticism. Thank you for pushing the boundaries of what's possible and for inspiring continuous innovation in the realm of artificial intelligence.

License

KLEP Software License (KSL-1.0)

Copyright (c) [2024] [Aron Irel Lewis Pence]

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

1. Attribution: All copies, modified or unmodified, of the Software must include the above copyright notice, this list of conditions, and the following disclaimer.
2. Ethical Use Clause: The Software shall be used for Good, not Evil. The user of the Software pledges not to use the Software for harm, including but not limited to promoting or engaging in illegal activities, violence, discrimination, or the infringement of basic human rights and freedoms. The determination of ethical use under this clause is left to the discretion of the Software creators and their appointed community, whose decisions on such matters will be considered final.
3. Contribution to Humanity Clause: Users of the Software are encouraged to contribute positively to humanity and the planet. This includes promoting peace, education, and environmental sustainability through the use of the Software.
4. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

KLEP’s Possible Use Case Scenarios:

Current Capabilities:

• With today's technology, KLEP has the potential to revolutionize various sectors. For instance, in education, it could create customized lesson plans for individual students in real time, adapting to their learning pace and emotional state. In healthcare and emergency response, KLEP could make informed, human-like decisions in triage and high-stress rescue scenarios, considering both ethical implications and emotional contexts. This capacity to blend cognitive and emotional intelligence in decision-making processes is what sets KLEP apart in today’s AI landscape.

5-Year Potential:

• Looking ahead, within the next five years, the KLEP system could significantly advance the fields of defense and scientific research. For defense applications, KLEP’s ethical and decision-making capabilities could allow for the development of self-regulating warheads that disarm upon detecting civilians or sensitive areas, such as schools, through onboard sensors. In scientific discovery, KLEP’s ability to process vast amounts of data could lead to groundbreaking advancements. For example, feeding a Limited Language Model or General Predictive Text Model with 17 billion data points could enable KLEP to identify potential cures for diseases like cancer, automating and accelerating the process of scientific discovery.

30-Year Vision:

• In a 30-year vision, KLEP’s sophisticated future-proofed and forward thinking framework, characterized by its emulation of human cognitive and emotional processes, presents a potential architecture for future android technology. This could lead to the creation of androids capable of human-like cognition and emotional intelligence, revolutionizing our interaction with technology and AI.

Technical Distillation::

KLEP (Key, Lock, Executable, Process) is a pioneering symbolic AI framework designed to replicate human cognitive processes. It uniquely integrates decision-making, ethical reasoning, and emotional intelligence in order to mimic human-like cognition. This sophisticated interplay is achieved through three main AI systems and one helper system: KLEP, Nora/aroN, and the Moral Compass - all of which are supported by SLASH.

1. KLEP: Anchoring the system, the KLEP operates on a stimulus-response mechanism. It navigates complex problem spaces by creating dynamic chains of action, triggered through a unique Key-Lock mechanism. This core is characterized by its agile assimilation of knowledge, enabling agents to spontaneously adapt to new situations or stimuli. This system is conceptually akin to my autonomous system, where dilation and constriction of blood vessels regulate the flow of blood cells.
2. Nora/aroN: Comprising two contrasting planning algorithms, Nora/aroN provides strategic depth. These algorithms offer advice in novel scenarios, enhancing decision-making flexibility. Currently I utilize A* and Monte Carlo Tree Search as my competing algorithms, but due to the modular nature of this system I can replace these when new technologies in graph search come about. This component symbolizes the system's capacity for reflection, long term planning, and strategic thinking - akin to human pondering.
3. Moral Compass: This component evaluates actions within ethical frameworks, translating moral assessments into a spectrum of emotions. It influences decision-making confidence but also fosters a conceptual self-awareness, situating the agent within a moral and temporal context.
4. SLASH: this serves as a pivotal communication bridge within the KLEP framework, functioning akin to the human brain's corpus callosum. It plays a crucial role in integrating various components of the KLEP system (including KLEP, Nora/aroN, and the Moral Compass) to ensure cohesive and synchronized operations.

KLEP's innovative approach to AI development is deeply rooted in its emulation of human neurological processes, drawing parallels from the intricate workings of my brain's neurons, receptors, and neurotransmitters. This biological inspiration is evident in the system's core functionality:

1. Executable Flow Management: Mirroring the brain's neuronal pathways, KLEP's executable flow management is akin to neural signal transmission. Actions in KLEP, like neurons in the brain, are triggered through specific stimuli (Keys) that activate corresponding responses (Locks), leading to a cascade of actions (processes). This mechanism reflects the synaptic transmission in the brain, where neurotransmitters (Keys) bind to receptors (Locks) to propagate nerve impulses.
2. Dynamic Re-evaluation and Adaptability: The system's ability to dynamically re-evaluate and adapt its behavior mirrors the brain's plasticity – its capacity to change and adapt in response to new information or experiences. In KLEP, the introduction of new Keys prompts a reassessment of current actions and strategies, similar to how the brain continuously processes and responds to new sensory inputs.
3. Integrated Cognitive Processes: The incorporation of Nora/aroN, the Moral Compass, and SLASH reflects the multifaceted aspects of human cognition, combining strategic planning, ethical reasoning, and emotional intelligence. This holistic approach is reminiscent of the brain's integrative function, where different regions collaborate to process information, make decisions, and regulate emotions.

In summary, KLEP stands out for its unique approach to AI that emulates the complex interplay of cognitive processes in the human brain. It harnesses the power of symbolic AI to create a framework where decision-making, ethical considerations, and emotional responses are intertwined, much like the interconnected networks of the brain. This level of sophistication in KLEP not only enhances its functionality but also brings it closer to achieving human-like cognition, paving the way for advanced AI applications in various sectors where nuanced, responsive, and ethically sound AI is indispensable

Central to KLEP’s architecture are numerous core scripts, each playing a crucial role in the system's functionality. These scripts collaborate seamlessly, reflecting the complexity and adaptability of human thought. Before delving into the specifics of each script, it's vital to understand the overarching philosophy behind KLEP: a commitment to creating an AI system that not only processes information but does so with an understanding akin to human cognition. This involves integrating decision-making, ethical considerations, emotional intelligence, and strategic planning. Each script within the KLEP framework contributes to this overarching goal, acting as a cog in a well-oiled machine that mirrors the nuanced workings of the human mind.

**KLEP Neuron: **

The KLEPNeuron script serves as the central node in the KLEP framework, managing knowledge acquisition, Key holding, and acting as a hub for various subsystem interactions. Here are its core functions and interactions:

1. Knowledge Acquisition: At the beginning of each frame, the KLEPNeuron scans its object hierarchy to identify new or absent child objects equipped with KLEPExecutableBase scripts. This process ensures that the neuron's knowledge is up-to-date and accurately reflects the current state of its internal environment.
2. Key Management: The KLEPNeuron collaborates with the SLASHKeyManager to manage Keys. Post knowledge update, it allows the SLASHKeyManager to release Keys that were buffered from the previous frame into the neuron by utilizing the Neurons Key element handling. This facilitates the dynamic flow of information and decision-making within the system.
3. Central Coordination Point: The KLEPNeuron acts as a central coordination point for various components of the KLEP system. It orchestrates updates and basic initializations among the SLASHKeyManager, SLASHBridge, KLEPAgent, and KLEPPlanner. This centralized approach ensures cohesive functioning and synchronization across different modules of the KLEP framework.
4. Executable Management: The script is responsible for adding and removing KLEPExecutableBase instances. When an executable is added, it is registered with the SLASHBridge and assigned to the planner for further processing. Similarly, when an executable is removed, it's unregistered, ensuring a clean and efficient management of executable elements.
5. Key Element Handling: The neuron script manages the addition and removal of KLEPKey elements. It adds new Keys to its collection (heldElements) and appropriately disposes of Keys that are no longer needed.

SLASH KeyManager:

The SLASHKeyManager is tasked with the management of Keys, which are vital elements in the system's decision-making process. Here's a breakdown of its primary roles and functionalities:

1. Key Creation and Management: The SLASHKeyManager generates and maintains Keys, crucial elements in unLocking actions within the system. It may employ ScriptableObject instances for Keys and Locks - though this is not a necessity for functionality. This quality of life addition offers an object-oriented approach for user interaction and modification. This design choice ensures flexibility and ease of use in manipulating Key properties.
2. Key Buffering and Injection: The script manages two categories of Keys – 'lazy' and 'excited'. Lazy Keys are stored in a buffer for consideration in the next frame, while excited Keys are injected directly into the neuron for immediate processing. This system allows for both delayed and instant Key activations, allowing designers to adapt to various operational requirements.
3. Perpetual Key Handling: Certain Keys, designated as perpetual, are immune to the standard Key lifecycle which typically ends each frame. These Keys represent persistent data elements, like ongoing player health or real-time environmental factors (temperature or elevation readings for example), and are crucial for maintaining continuous states within the system.
4. Key Trade and Exchange: The SLASHKeyManager notably facilitates the trade and exchange of Keys within and between KLEP systems. This function is particularly pivotal in complex KLEP configurations, where multiple neurons or agents, each with specialized roles, interact within a single system. For example, in a setup with several neurons and one agent, Keys are exchanged to adaptively manage decisions based on varying neuron inputs. This capability ensures a fluid transfer of information and authority, crucial for systems requiring coordinated, multi-faceted decision-making processes. The robustness of this feature makes it indispensable in scenarios demanding intricate data handling and dynamic response mechanisms. This concept draws inspiration from the collective behavior and distributed decision-making processes observed in multicellular organisms like slime molds.
5. Cleanup and Lifecycle Management: The script is responsible for the cleanup of Keys, ensuring data cleanliness and system efficiency. It appropriately disposes of Keys that are not marked for perpetuity, maintaining an organized and updated Key set for the system’s operation.

SLASHBridge is engineered as the central communication system within the KLEP framework, functioning much like a sophisticated network controller. It enables various components of the system to communicate effectively, ensuring that every action and response is perfectly timed. Here’s a detailed yet accessible breakdown of its Key functions:

1. Universal Event Handling: Think of SLASHBridge as a control center that manages signals (or 'events') across the system. It lets parts of the system like KLEPExecutableBase communicate important updates or actions. This process is crucial for keeping the entire system in harmony, responding to changes and commands in real-time.
2. Event Registration and Management: SLASHBridge operates like a dynamic directory, keeping track of all possible events and the specific actions they trigger. Components within the system can sign up (register) their events, and the bridge manages these, ensuring that the right action is taken whenever an event is raised.
3. Intra-Frame Communication: A notable feature of SLASHBridge is its ability to handle communications within the same operational frame. This is vital for immediate actions, such as when a task is completed by an executable, and other parts of the system need to be quickly informed for further processing.
4. Event Tracking and Logging: The bridge not only manages events but also keeps a detailed log of them, including where they came from and when. This feature is incredibly useful for monitoring the system's activities and troubleshooting any issues, ensuring a clear understanding of the system’s operational history.
5. Event Invocation Flexibility: SLASHBridge is designed to be adaptable, allowing the system to call upon (invoke) specific events as needed. This flexibility ensures that the system can respond to varying situations with the appropriate actions, making it highly responsive and versatile.
6. Modular Communication Backbone: The design of SLASHBridge allows it to act independently, without needing direct connections between different components. This modular approach enhances the system’s integration capabilities, enabling it to work seamlessly with various parts of the KLEP framework.

In essence, SLASHBridge is the cornerstone of effective communication within the KLEP system, ensuring that every component, from decision-making to action execution, operates in unison and with optimal efficiency. Its role is akin to a central nervous system in a living organism, coordinating and managing responses to internal and external stimuli.

KLEP Key and KLEP Lock:

KLEP Key and KLEP Lock are two fundamental components in the KLEP framework, interconnected through the IKLEPNexusElements interface. This shared interface standardizes their core attributes, ensuring cohesive interaction within the system. Here's a refined overview:

1. IKLEPNexusElements Interface: This interface defines the common properties shared by both Keys and Locks, namely Name and Attractiveness. This standardization facilitates seamless interactions and comparisons within the KLEP system, particularly in determining the desirability of different executable options with attraction values and the existence of Keys through their name.
2. KLEP Key: This represents the dynamic elements within the system, serving as triggers or enablers for certain actions. Keys hold not just their unique identifiers and attractiveness values, but also additional information relevant to decision-making processes, like their timestamp, a vector 3 for position, or associated transform objects. Special attributes like AllowsTrade and AllowsCopy add further versatility, allowing for more complex interactions and behaviors within the system.
3. KLEP Lock: Locks act as the conditions or prerequisites for executables. Each Lock is associated with a set of KLEPConditions that determine whether it's unLocked, signaling an executable's validation or readiness for execution. The Lock's attractiveness value, in combination with that of the Key, influences the overall appeal of executing a particular action, playing a critical role in the decision-making process.
4. KLEPCondition: A vital component of the KLEP Lock, the KLEPCondition class provides a flexible and robust way to define the criteria that must be met for a Lock to be considered unLocked. Conditions can range from the existence or absence of specific Keys to spatial relationships and comparative values. This versatility allows for creating intricate and nuanced Lock mechanisms.
5. Action Selection Influence: The combination of Keys and Locks, each with its attractiveness, directly influences the selection of actions within the KLEP system. Executables may return the sum of attractions from their associated Keys and Locks, guiding the selection process towards the most desirable action in a given context.

Klep Executable Base:

The KLEPExecutableBase serves as the foundational component for all executable entities within the KLEP framework, underpinning the system's dynamic decision-making capabilities. All specific implementations of executables inherit from this base class. Further, this script categorizes executables into four distinct types, each playing a unique role in the system's operation:

1. Action: These executables directly influence the environment or system based on the available Keys. They are crucial for executing the primary functionalities within the KLEP framework.
2. Goal: Goals orchestrate sequences of executables by issuing activation Keys, signaling a series of actions to unfold in a particular order. They possess enhanced capabilities, such as managing AI behaviors in layers, allowing for complex and nuanced sequences of executables. Remember, goals are also executables and thus may be managed by other goals.
3. Router: Routers transform and modify Keys, facilitating the conversion of one type of Key into another. This ability is vital for adapting the system's behavior based on evolving circumstances and requirements.
4. Sensor: Sensors generate Keys based on environmental inputs, serving as the system's means of perceiving and interacting with its surroundings.

Each executable is equipped to validate its readiness and capability to execute based on a two-step Lock mechanism. This mechanism ensures that an executable can only fire if it meets all necessary conditions (validation) and is capable of executing (execution). The absence of Locks is interpreted as perpetual readiness, commonly applicable to sensor-type executables for example.

A notable feature of the KLEPExecutableBase is its ability to signal the KLEPAgent for re-evaluation of previously invalidated or unexecutable executables through SLASH Bridge. This is crucial for managing dependencies between executables and ensuring cohesive system behavior and dynamic responses to inframe changes to the system. Example: Imagine Executive 1 eagerly awaiting a vital document from Executive 2 to proceed with its decision-making. Initially, Executive 1 evaluates the situation but can't proceed due to the missing document. As soon as Executive 2 completes its task and the essential document is delivered, the system promptly reassesses Executive 1's scenario. With the new information in hand, Executive 1 is now equipped to make an informed decision and take action.

Moreover, KLEP Executable Base maintains a list of Keys it generates upon completion. This list is instrumental for the SLASHPlanner to construct a comprehensive map of Key-Lock relationships, facilitating sophisticated planning algorithms like Nora (A*) and aroN (MCTS). By analyzing all potential Keys and their corresponding Locks, the system can infer the need for external Key injections, either from the environment or other KLEP systems.

In summary, KLEPExecutableBase is the core script that drives the behavior of all executable entities in the KLEP system, enabling a highly dynamic yet deterministic AI framework. Its robust design allows for complex sequences of actions, intricate planning, and adaptive behavior, making it an essential component of the KLEP AI model.

KLEP Goal:

KLEPGoal is an advanced script in the KLEP system, designed for intricate goal management. It extends the functionalities of KLEPExecutableBase to efficiently orchestrate goal-oriented action sequences.

Core Features of KLEPGoal:

1. Layered Execution with Patience Mechanism: The script employs a layered approach to manage executables. Each layer has specific execution requirements: some demand simultaneous firing of all actions within a single frame, while others allow actions to unfold over multiple frames. This flexibility introduces a strategic rhythm to goal achievement. The system also incorporates a patience mechanism, enabling it to wait and work on other tasks when conditions aren't right for action, mirroring human decision-making patience and timing.
2. Dynamic and Real-Time Management: KLEPGoal dynamically oversees executables within each layer, ensuring conditions are met before execution. It's equipped to handle real-time changes and errors, especially in layers with strict timing requirements, showcasing adaptability akin to human problem-solving.
3. Event-Based Monitoring and Response: The script is adept at monitoring events, like the completion of an executable, allowing real-time adjustments and responsive actions based on evolving conditions.
4. Self-Managed Executable Lifecycle: KLEPGoal autonomously manages the lifecycle of its executables. When added to the system, it checks for existing executables, adding them if absent. Upon removal, it cleans up by removing all added executables, maintaining the system’s integrity and coherence.
5. Integrated Cleanup Functionality: The script ensures efficient resource management and system performance by disposing of executables post-goal achievement or if the goal is terminated. This proactive cleanup prevents resource leaks, echoing the human brain's ability to streamline focus and resources.

In essence, KLEPGoal is akin to a human-like idea or cognitive function within the KLEP framework. Its design, emphasizing layered execution, adaptability, and self-managed lifecycle, mirrors aspects of human cognition and neural processing. This makes it not just a tool for AI goal management, but a bridge towards systems that reflect human-like understanding and responsiveness, applicable across diverse domains requiring nuanced AI interaction.

At this juncture, with only minimal modifications to the covered systems, KLEP can be operational and demonstrate its capability to adeptly navigate intricate problem spaces, delivering deterministic, real-time solutions for diverse and dynamic environments. It is applicable to a wide breadth of domains - from video games and automated call centers to medical monitoring, military operations, LLM and GTP technologies, and beyond. Yet, this represents just the foundational aspect of KLEP. The subsequent sections will explore how KLEP transcends from being merely a revolutionary symbolic AI tool into a transformative force with the potential to redefine human interaction with intelligent systems.

KLEP Agent:

The KLEP Agent is responsible for navigating through complex problem spaces and making decisions based on available Keys in each frame. Here's a detailed breakdown of its functionality:

Key Functionalities of KLEP Agent:

1. In-Tandem and Solo Action Execution: The Agent prioritizes executing 'in-tandem' actions – those that can occur simultaneously. It systematically assesses and unLocks these executables, then revisits previously ignored actions for potential execution. Subsequently, it selects one 'solo' action to execute, guided by the combined attractiveness of Keys and Locks. A 'stickiness' threshold, akin to a neuron's chemical firing threshold, moderates this selection process.
2. Learning Reinforcement through Q-Values: The Agent utilizes Q-Values for learning reinforcement, which influences the attractiveness value of each action. These values enable the Agent to make informed, confident decisions in familiar scenarios. This learning mechanism is akin to neuroplasticity in human brains, where repeated successful actions strengthen certain neural pathways.
3. Certainty and Adaptive Planning: The Agent's decision-making confidence is quantified in its 'certainty' metric. When certainty drops below a threshold, the Agent seeks strategic guidance from the Planner for long-term and strategic decisions. This flexibility allows the Agent to align with the Planner’s objectives or explore better options based on newfound confidence and opportunities.
4. Integration with Moral Compass and Slash Memory: The Agent's actions are informed by ethical considerations, thanks to inputs from the Moral Compass. This system evaluates actions within moral frameworks, influencing the Agent’s confidence in its choices. Slash Memory provides a historical context, helping the Agent learn from past experiences and modify its behavior accordingly.
5. Trauma Handling and Memory Capture: The Agent is equipped to handle traumatic experiences by capturing snapshots of such events using Slash Memory. This feature allows the system to adapt and respond to significant or impactful events more effectively in the future.
6. Dynamic Execution and Reset Mechanism: The Agent dynamically executes actions and has a reset mechanism for its current action, ensuring readiness for new decision cycles. This mimics the human brain's ability to shift focus and adapt to new stimuli or changes in the environment.

The KLEP Agent is a sophisticated AI entity, balancing immediate needs with strategic planning and ethical considerations. Its multi-faceted approach to problem-solving, underpinned by a blend of reinforcement learning, certainty, and moral judgment, mirrors human cognitive processes, making it a highly advanced component in the KLEP framework.

SLASH Memory:

SLASH Memory in the KLEP framework operates as an intricate memory management system, emulating aspects of human memory processing. It combines a modified Least Recently Used (LRU) Cache mechanism for short-term memory management with a HashSet structure for maintaining long-term memories. This dual-structure approach facilitates efficient memory storage and retrieval, critical for the system's adaptive learning and decision-making processes.

1. **Short-Term Memory Management: **The short-term memory component operates on a heatmap basis, where 'experiences' or 'events' gain 'heat' with repeated occurrences. This heatmap approach aims to allow multiple similar experiences to contribute collectively to the heat of a memory, enhancing the system's ability to recognize and prioritize recurring patterns or events. Once an experience accumulates sufficient heat, it transitions into the long-term memory, thereby optimizing space within the short-term memory cache.

2. **Long-Term Memory and Traumatic Experiences: **Long-term memory in SLASH Memory is managed through a HashSet, ensuring quick access and efficient storage. To emulate the human aspect of forgetting, a limit is set on the number of memories retained. When this capacity is reached, the system overwrites the oldest memories, reflecting the natural ebb and flow of memory retention in human cognition.

   Additionally, SLASH Memory features a unique mechanism for handling 'traumatic' or highly significant experiences. These events can bypass the standard short-term memory process, being directly stored into long-term memory. This feature ensures that pivotal experiences have a lasting impact on the system's learning and decision-making algorithms.

3. **Integration with Decision Making: **SLASH Memory plays a role in informing the KLEP Agent's decision-making process. By providing a historical context of past successes and failures, it allows the Agent to adjust its Q-Values and strategies in real-time. This capability makes the system adept at learning from past interactions, continuously refining its approach to navigate complex problem spaces more effectively.

In summary, SLASH Memory is a sophisticated component of the KLEP framework, enabling nuanced memory management that closely mirrors human cognitive processes. Its integration with the Agent's decision-making system underscores its importance in the overall AI architecture, contributing significantly to the framework's dynamic and adaptive capabilities.

SLASH Planner And Nora/aroN:

The SLASH Planner serves as the strategic core of the KLEP system, bridging the gap between the agent's myopic decision-making and the advanced search algorithms of Nora and aroN. Its primary role is to act as a comprehensive navigator, organizing the problem space and directing the agent towards effective solutions when requested to do so. This centralization simplifies the system's complexity while leveraging two sophisticated algorithmic searches.

1. Curating the Problem Space Map:

   At the heart of the Planner's function is its ability to build a detailed and searchable graph of the problem space. This is achieved by collating information from all executables about the Keys they can generate and the Locks they need to open. This extensive mapping allows the Planner to accurately represent the current state of the system and the potential pathways to desired outcomes. Any Keys that are required by Locks which are not created in the system are expected to be Keys that are injected from external sources, such as an enemy taking damage and receiving a “Hurt_Key”.

2. **Dynamic Algorithmic Search: **

   The Planner utilizes two contrasting search algorithms: Nora, which employs an A* algorithm favoring a breadth-first approach, and aroN, which uses Monte Carlo Tree Search (MCTS) for a depth-first exploration. This dual approach ensures a balanced exploration between experimental and exploitative strategies, providing a comprehensive view of potential solutions.

3. **Algorithmic Collaboration and Evolution: **

   An innovative aspect of the Planner is the potential adversarial relationship between Nora and aroN. By implementing a reward system that recognizes historical success, the Planner encourages a competitive dynamic that can enhance the efficiency and effectiveness of the proposed solutions. Moreover, this framework's flexibility allows future integration of more advanced algorithms as they become available, ensuring the Planner's continued evolution and relevance.

4. **Nora/aroN Synergy for Optimal Solutions: **

   The synergy between Nora and aroN within the Planner is crucial. Nora's A* algorithm excels in systematically exploring the problem space, efficiently identifying viable paths. Meanwhile, aroN's MCTS algorithm delves deeper into specific strategies, thoroughly evaluating their potential success. The Planner evaluates these dual perspectives, offering the agent a well-rounded and strategically sound plan.

5. **Guiding the Agent with Subtlety: **

   Unlike traditional planning systems that dictate a fixed set of actions, the SLASH Planner influences the agent's decision-making more subtly. It enhances the attractiveness of certain Keys and Locks, thereby 'polishing' pathways that were previously less appealing. This method of 'guiding' rather than 'directing' allows the agent to choose paths dynamically, making the process less rigid and more responsive to changing circumstances. The agent, influenced by this nuanced guidance, is more likely to select a path that aligns with the Planner’s strategic insight yet retains the flexibility to adapt as new information or conditions arise.

6. **Future-Proofing with Flexibility: **

   The SLASH Planner's design accommodates future advancements in AI and search algorithms. Its architecture is prepared to integrate more sophisticated methods as they emerge, making it a future-proof component in the KLEP system. This forward-thinking approach ensures that the KLEP framework remains at the forefront of AI development, adaptable to evolving technological landscapes.

The SLASH Planner serves as a pivotal component within the KLEP system, functioning as both a navigator and an arbiter in the realm of strategic decision-making. It effectively manages the dynamic interplay between the Nora and aroN algorithms, allocating resources preferentially—such as permitting more in-depth analysis steps to the algorithm demonstrating greater reliability. This selective empowerment, customizable according to the designer's requirements, underscores the Planner's adaptability. While this nuanced strategic orchestration is a key feature, it's important to note that the Planner's sophisticated long-term planning capabilities are not essential for the system's fundamental operations. In essence, the SLASH Planner operates akin to an abstract guiding force, synthesizing the insights from the metaphorical 'angels and demons'—Nora and aroN—each vying for influence. This synthesis is then translated into actionable strategies for the agent, striking a balance based on the most effective counsel.

Emotion Integration in KLEP:

The KLEP system's innovative approach to emotional processing, KLEPEmotion, represents a pioneering step towards integrating emotional intelligence into AI. This module is designed to interpret and react to emotions, grounding them in moral and ethical evaluations. It hypothesizes that emotions, in essence, are abstract reflections of my internal moral and ethical standards, continuously influencing and adjusting my actions and decisions while broadcasting that evaluation to those around us.

Key Components and Dynamics:

1. 2D Vector Representation of Emotions: Emotions are quantified as two-dimensional vectors. The x-axis denotes the hedonic aspect (pleasure-displeasure), while the y-axis represents arousal levels (activation-deactivation). The vector's magnitude indicates the intensity or force of the emotional response, providing a straightforward way to represent complex emotional states.
2. Historical Analysis of Emotions: KLEPEmotion maintains a log of emotional vectors to track the evolution of emotional states over time. This history is vital for understanding emotional continuity and stability, assessing the duration and consistency of emotions like prolonged joy or sadness, or erratic shifts of mania and depression.
3. Emotional Stability Metric: Stability is evaluated based on the variations in emotional vectors. Smaller, consistent vector changes imply stability, while larger, erratic fluctuations indicate instability. This metric offers insights into the system's psychological equilibrium, reflecting the robustness of the emotional processing.
4. Dynamic Emotional Processing: The system employs Emotional Inertia and Emotional Friction to create realistic emotional responses. Emotional Inertia smooths the transition between emotions, whereas Emotional Friction gradually reduces emotional intensity over time, simulating the natural decay of human emotions. The inspiration of this rests in steering behaviors, whereas those deal with physical motion, this deals with emotional motion.
5. Behavioral Adaptation: The system adapts its behavior based on its current emotional state and stability. For instance, in times of high emotional instability, it may prioritize actions aimed at achieving emotional balance or easy to obtain goals for, in essence, dopamin.

Implications and Future Directions:

This framework offers a pragmatic approach to integrating emotional intelligence in AI, particularly in the context of the KLEP system. The current 2D vector model, while simpler than Plutchik's Wheel of Emotions, provides a solid foundation for a responsive and empathetic AI system. Future developments may explore more complex emotional models or include additional dimensions to capture a broader emotional spectrum. The integration of this module into KLEP promises significant advancements in applications requiring nuanced ethical and emotional understanding.

The KLEPEmotion module represents an ambitious endeavor to humanize AI, providing it with the capacity to interpret, evaluate, and adapt to emotional states. Its inclusion in the patent application highlights the forward-thinking and innovative nature of the KLEP system, showcasing its potential to revolutionize AI's interaction with human emotions and ethical standards.

Ethics Integration in KLEP:

In the KLEP system, the integration of ethical decision-making represents a crucial component, particularly in the realm of AI. The KLEP Ethics module is designed to imbue the system with a structured code of conduct, enabling it to make decisions that are not only efficient but also adhere to predefined moral standards. This inclusion of ethical processing is a testament to KLEP's commitment to responsible and human-centric AI development.

Key Aspects and Functionality:

1. Codification of Ethical Standards: The KLEP Ethics module is built on the principle that ethical norms can be codified into a set of rules or guidelines. These standards are essential in guiding the system's decision-making processes, ensuring that actions are evaluated not just on efficiency or outcome but also on moral grounds.
2. Dynamic Ethical Decision-Making: The module dynamically interprets and applies ethical codes to all scenarios. This flexibility allows the system to adapt to various contexts, ensuring that ethical considerations remain central to its operations, whether in simple tasks or complex problem-solving scenarios.
3. Interplay with Emotional Processing: The ethical evaluations made by the KLEP Ethics module are integral to the system's emotional responses, as conceptualized in the KLEPEmotion module. Ethical assessments influence the emotional state of the system, reflecting the psychological interplay between moral reasoning and emotional reactions in humans.
4. Customizable Ethical Frameworks: Recognizing the diversity in moral philosophies, KLEP Ethics allows for the customization of its ethical framework. This adaptability ensures that the system can align with various ethical standards, from Kantian deontology to utilitarianism, catering to different applications and user preferences.
5. Impact on Behavioral Outcomes: Decisions influenced by the KLEP Ethics module affect not only the system's immediate actions but also its long-term behavioral patterns. By incorporating moral considerations, the system is designed to act in a manner that upholds ethical integrity, enhancing trust and reliability in its interactions.

Implications and Future Developments:

Integrating ethics into the KLEP system represents a significant stride in AI development, ensuring that intelligent systems can operate within a moral framework. This model’s potential to influence AI behavior in ethically sound ways is profound. Future advancements may involve refining the ethical algorithms for more nuanced moral reasoning or expanding the ethical database to include a wider range of moral philosophies.

In conclusion, the KLEP Ethics module is a visionary component of the KLEP system, underlining the importance of ethical considerations in AI. Its development and integration into KLEP will be instrumental in shaping AI systems that are not only intelligent and responsive but also morally attuned and responsible. Including this module in the patent application highlights KLEP's commitment to ethical AI, showcasing its potential as a leader in developing AI systems that are in harmony with human values and ethical principles.

Conclusion and Future Developments Trajectory:

While the foundational aspects of the KLEP system are operational and demonstrate significant progress, my vision extends beyond the current capabilities. The roadmap for future developments includes substantial enhancements and refinements, ensuring the system's growth in sophistication and applicability.

1. Advancements in KLEP Ethics and KLEP Emotion:

• The KLEP system currently integrates basic functionalities of ethical decision-making and emotional processing. I are actively working towards the full implementation of KLEP Ethics and KLEP Emotion modules. These advancements are aimed at enriching the system's ability to make decisions that are not only logically sound but also ethically responsible and emotionally intelligent. The development includes rigorous coding, experimentation, and iterative refinement to ensure these modules are not only theoretically sound but also practically effective in real-world scenarios.

2. Development of User-Friendly Visualizers:

• Recognizing the complexity of the KLEP system, particularly in its planning and decision-making processes, I are developing intuitive visualizers. These tools will make the system’s operations more transparent and comprehensible to human users. The visualizers are designed to illustrate how the system processes information, makes decisions, and evolves over time, thereby making the technology more accessible and user-friendly.

3. Experimentation and Evaluation:

• To validate the effectiveness of the newly developed features, extensive experimentation and evaluation are planned. This involves testing the system in various scenarios to ensure its reliability and efficiency. The goal is to move beyond theoretical models to practical, tested solutions that demonstrate real-world applicability and robustness.

4. Continuous Refinement and Expansion:

• The development of the KLEP system is an ongoing journey. I are committed to continuous refinement and expansion of its capabilities. This includes not only enhancements to existing features but also the exploration of new functionalities that could further elevate the system's performance and scope.

5. Adherence to Emerging Technological Standards:

• As technology evolves, so will the KLEP system. I are dedicated to keeping abreast of the latest advancements in AI and machine learning, ensuring that my system remains compatible with emerging standards and practices in the field.

In conclusion, the future developments for the KLEP system are grounded in a commitment to innovation, practical applicability, and technological excellence. These advancements are anticipated to significantly enhance the system's capabilities. This forward-looking approach underscores my dedication to creating a dynamic, adaptable, and cutting-edge AI system.