This article is based on the Open Source Platform Tools4AI

With Large Action Model , ensuring accurate decision-making and mitigating potential risks are paramount concerns. To address these challenges, a cutting-edge approach known as the Multi-AI Voting Mechanism has been added to Tools4AI project. This mechanism involves soliciting votes from multiple AI systems to determine the suitability of an action based on a given prompt. In this article, we delve into how this mechanism operates and its implications for augmenting AI decision-making processes.
Introduction: AI-driven systems are increasingly being integrated into various domains, from customer service applications to industrial automation. However, the accuracy and reliability of AI decision-making remain critical issues. To enhance the robustness of AI systems, the Multi-AI Voting Mechanism introduces a collaborative approach, leveraging the collective intelligence of multiple AI models.

Understanding the Multi-AI Voting Mechanism: In Tools4AI the Multi-AI Voting Mechanism involves the coordination of three distinct AI systems: Gemini, OpenAI, and a LocalAI instance. When presented with a prompt and a list of comma-separated actions, each AI system independently evaluates the proposed actions in the context of the prompt. If all three AI systems agree that a particular action is suitable based on the prompt, they proceed to the next step.

Agreement on Parameters: Once a consensus is reached regarding the action, the AI systems move on to evaluate the parameters associated with it. Parameters define the specifics of the action, such as target entities or desired outcomes. Each AI system independently assesses the proposed parameters and votes on their validity. Unanimous agreement among the three AI systems signifies confidence in the chosen parameters.

Human Decision-Making in the Loop: While AI systems play a crucial role in the decision-making process, human oversight remains indispensable, especially in high-stakes scenarios. If the Multi-AI Voting Mechanism is configured to include human intervention, the final decision rests with a human operator. In this case, the AI systems provide recommendations based on their votes, and the human operator makes the ultimate call.

Benefits of the Multi-AI Voting Mechanism:
Enhanced Reliability: By leveraging multiple AI systems, the mechanism reduces the likelihood of erroneous decisions.
Risk Mitigation: Collective agreement among AI models minimizes the risk of harmful actions or errors.
Adaptability: The mechanism can accommodate a wide range of prompts and actions, making it versatile across various applications.

Transparency: The decision-making process is transparent, allowing stakeholders to understand how actions are determined.
Real-World Applications: The Multi-AI Voting Mechanism can find applications across diverse domains, including healthcare, finance, and autonomous vehicles. For example, in a healthcare setting, AI systems can collaborate to recommend treatment options for patients based on medical history and symptoms.

Example 1
Prompt: "Schedule a maintenance appointment for my car."
List of Actions:

1. scheduleMaintenance(car, date)
2. bookAppointment(mechanic, car, date)
3. reserveServiceSlot(car, date)
4. confirmAppointment(car, mechanic, date)

Scenario:
Gemini: Gemini, the first AI system, analyzes the prompt and evaluates the list of actions. It considers the context of the request and identifies the most suitable action. In this case, Gemini agrees that "scheduleMaintenance(car, date)" is the appropriate action based on the prompt.

OpenAI: OpenAI independently reviews the prompt and the list of actions. It also determines that "scheduleMaintenance(car, date)" aligns best with the user's request.

Local AI: The LocalAI instance, configured to understand specific domain knowledge or context, assesses the prompt and actions. It reaches the same conclusion as Gemini and OpenAI, supporting the selection of "scheduleMaintenance(car, date)."

Voting Process: With unanimous agreement from all three AI systems, the Multi-AI Voting Mechanism proceeds to the next stage. Each AI system casts its vote in favor of

"scheduleMaintenance(car, date)," indicating consensus on the chosen action.
Parameters Evaluation: Once the action is determined, the AI systems examine the parameters associated with it, such as the car's details and the desired appointment date. They independently verify the validity of these parameters and cast their votes accordingly.
Final Decision: If human intervention is included in the decision-making process, the AI systems present their recommendations to the human operator. Based on the collective agreement of the AI systems and the operator's judgment, the final decision is made to schedule the maintenance appointment for the car.

Example 2
Prompt: "I need to book a flight from New York to Los Angeles."
List of Actions:

1. BookFlight
2. FindAlternativeRoutes
3. CheckFlightAvailability
4. CancelFlight

Scenario: When the prompt and list of actions are sent to the Gemini, OpenAI, and Local AI systems, each AI evaluates the suitability of the actions based on the given context.
Gemini AI: Analyzes the prompt and actions, considering factors such as user intent, available options, and potential outcomes. It determines that "BookFlight" is the most appropriate action based on the user's request.

OpenAI: Utilizes its vast language processing capabilities to understand the nuances of the prompt and actions. It also identifies "BookFlight" as the optimal action, aligning with Gemini AI's assessment.

Local AI: Leverages domain-specific knowledge and context to evaluate the actions. It concurs with Gemini and OpenAI, recognizing "BookFlight" as the most suitable action given the user's request.

Result: All three AI systems agree that "BookFlight" is the appropriate action based on the prompt. This collective consensus demonstrates a high degree of confidence in the chosen action, increasing reliability and ensuring user satisfaction.

Example 3
Prompt: "I need to schedule a meeting with the team tomorrow."
List of Actions:

1. ScheduleMeetingAction
2. CreateEventAction
3. ReserveConferenceRoomAction

Gemini's Response: Gemini suggests scheduling a meeting using its internal calendar integration.
OpenAI's Response: OpenAI recommends creating an event on the team's shared calendar.
LocalAI's Response: LocalAI proposes reserving a conference room for the meeting.

In this scenario, each AI system has interpreted the prompt differently based on its algorithms and training data. Gemini focuses on scheduling using its integrated calendar system, OpenAI suggests creating an event on a shared calendar, and LocalAI recommends reserving a physical conference room.

Now, let's say the voting process begins:
Gemini: Supports the ScheduleMeetingAction with parameters for the meeting time and attendees.
OpenAI: Supports the CreateEventAction with parameters for event title, date, and attendees.
LocalAI: Supports the ReserveConferenceRoomAction with parameters for the room, date, and duration.

Since all three AI systems have proposed different actions and parameters, the voting process doesn't reach a consensus. In such cases, the system might escalate the decision to a human-in-the-loop for manual intervention. The human reviewer can analyze the suggestions from each AI system and make an informed decision based on the context of the prompt and the organization's policies.

**Conclusion: **The Multi-AI Voting Mechanism represents a significant advancement in AI decision-making, leveraging the collective intelligence of multiple AI systems to ensure accuracy and reliability. By soliciting votes from AI models and, when necessary, human operators, this mechanism strikes a balance between automation and human oversight, paving the way for safer and more trustworthy AI-driven processes. As AI continues to play an increasingly central role in various industries, the Multi-AI Voting Mechanism stands as a testament to the ongoing evolution of AI technologies.