In the realm of artificial intelligence (AI), understanding human behavior and decision-making processes has always been a fascinating challenge. A simple yet profound scenario—a person with a sweet tooth craving a specific sweet from a particular shop, only to find it unavailable—offers a perfect illustration of the complexity AI models face in mimicking human decision-making. This individual’s choices, which may vary from:

– avoiding the sweet altogether,
– selecting an alternative sweet from the same shop, or
– seeking the desired sweet at another shop

highlight the nuanced and context-dependent nature of human decisions.

So , let’s dive in the very challenge posed while making contextual Decision-Making

The key to replicating this decision-making process in AI lies in the ability to model not just the decisions themselves, but the context and factors influencing these decisions. Contextual decision-making in AI is about understanding the ‘why’ behind each choice and the factors that alter this decision in different directions. It requires a model that can weigh various factors, such as urgency, preference, availability, and past experiences, to predict or decide in a manner that aligns with human behavior.

Welcome! Vector Models – best suited for understanding preferences and similarities. Vector models, particularly those based on embedding techniques like Word2Vec or GloVe, offer a way to represent entities (such as sweets, shops, or preferences) in a multi-dimensional space. In this space, the distance and direction between vectors can signify similarity or preference. For example, different sweets can be represented as vectors in this space, and a person’s preference can be modeled as a vector pointing towards their favored sweet. The absence of the desired sweet at the preferred shop can be represented as a vectorial deviation, and the AI’s task is to find the vector (decision) that realigns with the person’s preference vector as closely as possible under the given context.

Vector Models fight for their superiority against Graph Models used for mapping relationships and influencing factors. Graph models excel at representing complex relationships and dependencies between entities and decisions. In our scenario, a directed graph could model entities (people, sweets, shops) as nodes and the relationships or decisions (preferences, availability, choices) as edges, with weights representing the strength or importance of these relationships. Factors like urgency and importance can dynamically adjust these weights, influencing the decision path the model predicts or recommends.

For instance, a graph model can represent the decision-making process as a pathfinding problem. Nodes represent the person, potential sweets, and shops, while edges represent the possible decisions (e.g., choosing a different sweet, going to another shop). The weights on the edges could be influenced by factors such as urgency (how badly does the person want the sweet?) or convenience (how easy is it to go to another shop?), thus guiding the AI in predicting the most likely decision under the given circumstances.

Let’s add the spice called Contextual Awareness to this recipe for making it AI driven.

To effectively mimic human decision-making, AI models need to integrate context awareness, understanding that the same person might make different decisions under different circumstances. This involves dynamically adjusting the model’s parameters (vector directions, graph edge weights) based on the current context, past behaviors, and the relative importance of various factors including personas.

I’ll try to conclude this interesting topic by just highlighting the underlying mathematical foundations which exist for decades if not centuries.

The mathematical models underpinning these AI decision-making processes involve complex algorithms for vector space manipulation and graph theory. Techniques such as cosine similarity for vector models or Dijkstra’s algorithms for graph models can be used to evaluate decision options and predict outcomes. Of course fine-tuning these is nothing less than a perfect blend of art and science where human and machine intelligence marry each other to produce a wonderful product.

So , in conclusion , the sweet shop dilemma underscores the importance of context and variability in human decision-making. By leveraging vector and graph models, AI can better understand and predict human behavior, offering insights not just for simple decisions but for complex, context-driven choices that define our everyday lives. As AI continues to evolve, the ability to accurately model and respond to the nuanced preferences of individuals will be crucial for developing more intuitive, human-like AI systems and one day in the distant future the walls will diminish.

Happy Reading!!

Author – Sumit Rajwade, Co-founder: mPrompto