Handwritten Passwords for Touchscreen Devices

Introduction to the Belief-Desire-Intention (BDI) Model
Okay, let’s dive into the Belief-Desire-Intention (BDI) model. It sounds kinda complicated, but trust me, it’s not that bad. 😉
Ever wonder how we make decisions? The BDI model tries to mimic that. It’s a cognitive architecture – fancy words, I know – that helps ai agents act like they’re thinking. The Belief–desire–intention software model is based on the way humans reason – that’s straight from Wikipedia.

Beliefs: These are what the agent thinks is true about the world. It’s what the ai is working with.
Desires: What the ai wants to achieve. Think of these as goals. A desire of the ai is typically a description of a desired state of the environment.
Intentions: The plans the ai commits to. It’s like saying, “Okay, I believe this, I want that, so I’m going to do this.”

Basically, BDI helps ai systems handle uncertainty and adapt to changes. It’s about making decisions when things aren’t crystal clear. As ctoi puts it, it’s essential for building systems that gracefully handle the messiness of the real world.
As promised, let’s dive deeper into each of these core components: beliefs, desires, and intentions.
The Three Core Components of the BDI Model
Okay, so you’re probably wondering what all the fuss is about with these beliefs, desires, and intentions, right? It’s more than just some fancy AI jargon, I promise. It’s about getting ai to act a little more like us – or at least, how we think we act. 😉
Beliefs are basically what the ai thinks is going on in the world. It’s its current understanding of things. Think of it as the ai’s internal model.

For example, in a self-driving car, the ai might believe that there’s a pedestrian crossing the street. But what if the sensor is faulty and it’s just a trash can blowing in the wind? The ai’s gotta handle that uncertainty.
Beliefs aren’t static either, they are constantly updating as the ai gathers new information. Imagine a retail ai that believes a certain product is popular based on initial sales data – but then sales tank. The AI needs to revise its beliefs.

Desires are the ai’s goals, what it’s trying to achieve. It’s not just about having one simple objective, though. It can be multiple, complex, and sometimes even conflicting goals.

Think of a customer service chatbot. It might desire to resolve the customer’s issue quickly, but also desire to provide accurate information. Balancing those two can be tricky.
In finance, an ai trading system might desire to maximize profits, but also desire to minimize risk. So it’s all about prioritizing and finding the right balance.

Intentions are like the ai’s committed plans, the specific actions it’s decided to take. It’s the bridge between its desires and its actual behavior – basically, how it puts its beliefs and desires into action.

Consider a supply chain ai that intends to reroute a shipment due to a weather delay. It’s not just a random decision; it’s based on its belief about the weather and its desire to minimize disruptions.
Intentions also have a certain “stickiness” to them. The ai doesn’t just abandon a plan at the first sign of trouble. It sticks with it unless something major changes.

So, that’s the basic breakdown of beliefs, desires, and intentions. It’s a way to give ai a bit more “common sense” and the ability to adapt to changing situations.
Next up, we’ll look at how these components actually work together in practice.
A Detailed BDI Scenario: The Smart Home Assistant
Let’s walk through a scenario to see how a BDI agent, say, a smart home assistant, operates from start to finish.
Scenario: It’s a chilly evening, and the user has just arrived home.

Belief Update:

The agent’s sensors detect the front door opening and the user’s presence.
It checks the internal thermostat reading and notes the current temperature is 18°C.
It accesses its internal calendar and knows it’s currently 7 PM.
It recalls the user’s preference: “I like it warm when I get home from work.”

Option Generation: Based on its beliefs, the agent considers several possible actions:

Turn on the living room lights.
Turn up the thermostat to 22°C.
Play some relaxing music.
Ask the user if they want a cup of tea.
Do nothing.

Filtering: The agent filters these options based on its current intentions and context.

Intentions: The agent might have an existing intention to “maintain user comfort.”
Context: The user has just arrived home, it’s evening, and the temperature is low.
The agent filters out “do nothing” because it conflicts with the “maintain user comfort” intention given the low temperature. It also prioritizes actions that directly address the user’s immediate comfort. Turning on lights and adjusting the thermostat are more immediate needs than playing music or offering tea.

Deliberation: The agent now deliberates among the remaining options. It has a strong desire to “ensure user comfort,” and its belief is that the house is too cold.

The desire to “ensure user comfort” is prioritized.
The belief that “the house is cold” strongly supports the action of turning up the thermostat.
The desire to “make the user feel welcome” supports turning on lights.
The agent decides that adjusting the thermostat is the most critical action for immediate comfort, and turning on lights is a secondary but still important action. It forms a new intention: “Set thermostat to 22°C.” It might also form an intention to “Turn on living room lights.”

Execution:

The agent sends a command to the thermostat to increase the temperature to 22°C.
It sends a command to turn on the living room lights.

Cycle Repeats: As the thermostat adjusts the temperature, the agent will continue to monitor its beliefs. If the temperature reaches 22°C, its belief about the house being cold will be updated, and the intention to “Set thermostat to 22°C” might be considered complete. New desires or updated beliefs could trigger further cycles. For instance, if the user then says, “I’m feeling a bit peckish,” a new desire (“satisfy user hunger”) would be formed, leading to new options and intentions.

This scenario illustrates how beliefs inform desires, which then lead to prioritized intentions that are executed, and how the cycle continuously updates based on new information and evolving goals.
The BDI Execution Cycle: How Agents Act
Okay, so how do these BDI agents actually do stuff? It’s not just about having beliefs and desires floating around, right? 😉

Belief Update: First off, the agent’s gotta keep up with the world, incorporating new info from sensors or other sources. Like, if a self-driving car’s lidar detects a new obstacle, it updates its belief about the road ahead.
Option Generation: Then, it brainstorms possible actions based on its updated beliefs. For instance, a smart thermostat might consider turning up the heat, sending an alert, or doing nothing, depending on the current temperature.
Filtering: Not all options are good ones, so the agent filters ’em based on current intentions and context. The agent considers its current intentions (e.g., “maintain user comfort”) and the broader context (e.g., time of year, user location). For example, a retail ai wouldn’t suggest a winter coat to someone in Miami in July, because that context conflicts with the likely desire for appropriate clothing.
Deliberation: After, it’s decision time – choosing the next intention to pursue. It’s like deciding whether to focus on speed or accuracy, or maybe a mix of both. This involves weighing desires against each other and against the current beliefs about the world. For instance, if an agent has conflicting desires to “minimize cost” and “maximize speed,” it will deliberate to find a balance or prioritize one based on the current situation.
Execution: Finally, the agent does something, taking action based on its selected intention. The self-driving car changes lanes, the thermostat adjusts the temperature, or the retail ai displays relevant items.

So, that’s the cycle in a nutshell. It’s how BDI agents manage to be both reactive and, like, thoughtful, responding to changes while keeping their long-term goals in mind. Next up, we’ll see how all this plays out in practice.
Real-World Applications of the BDI Model
Okay, so, you know how sometimes AI seems kinda…out of touch with reality? Like it’s missing some common sense? Well, the Belief-Desire-Intention (BDI) model tries to fix that – at least a little.
The BDI model is finding its way into some pretty cool applications. Think of it as giving AI a bit of a human-like thought process. It’s not just about reacting; it’s about understanding, planning, and, most importantly, adapting to whatever curveballs the world throws at it.

Autonomous Vehicle Navigation Systems: Imagine a self-driving car cruising down the road. It’s not just blindly following GPS; it’s constantly updating its beliefs about its surroundings – pedestrians, traffic lights, other cars. Then, it desires to get you to your destination safely and efficiently. Finally, it intends to take the best route, adjusting as needed based on new info.
Smart Grid Management: Ever wonder how power grids balance energy supply and demand? Well, AI using the BDI model can help. These systems believe things like energy demand, generation capacity, and potential outages. They desire to minimize costs and ensure reliability. And they intend to implement specific load-balancing plans, tweaking them as conditions change.

It’s not a perfect solution, of course. The Belief–desire–intention software model has limitations and criticisms, including a lack of specific mechanisms for certain aspects of reasoning. But the BDI model offers a way to build ai that’s more than just reactive; it’s proactive, thoughtful, and able to handle the unexpected.
So, what’s next? Let’s look at putting it all together.
When to Use the BDI Architecture
Okay, so when should you actually use this BDI architecture? It’s not a one-size-fits-all kinda thing, you know? 😉

Incomplete or uncertain info? BDI shines. Think of a supply chain ai trying to reroute shipments during a natural disaster. BDI’s belief revision mechanisms allow it to continuously update its understanding of the situation as new, often conflicting, information comes in, enabling it to make the best possible decisions with what it has.
Conflicting goals? No prob. A personal finance ai might balance desires for saving and spending, helping you decide where to cut back without sacrificing all the fun. BDI agents can represent multiple desires simultaneously and use deliberation mechanisms (like utility functions or goal hierarchies) to prioritize or resolve conflicts between them.
Plans need persistence? BDI’s got you. Consider a project management ai that sticks to its schedule despite minor setbacks. Intentions in BDI have a degree of “stickiness,” meaning the agent will try to pursue them unless there’s a strong reason to abandon them, providing a sense of commitment to plans.
Need to explain things? BDI’s great, cause its beliefs, desires, and intentions are easy to understand and explain why the ai did what it did. The explicit representation of these mental states makes the agent’s reasoning process more transparent and interpretable.

Next up, let’s look at the limitations.
Conclusion: The Power of Understanding, Planning, and Adapting
Okay, so, what’s the deal with the BDI model? Well, it’s all about getting ai to understand, plan, and, yup, adapt!

It’s a shift away from just reacting to stuff. Instead, it’s about agents really understanding what’s going on. I mean, if ai’s going to thrive in, like, the real world, it needs to understand all that uncertainty.
This model is all about creating ai that can handle complexity. We’re talking systems that can think on their feet, not just follow instructions blindly – capabilities that are essential for modern ai systems.

So, yeah, the BDI model? It’s kinda a big deal if you’re into making ai that’s actually, well, intelligent.

*** This is a Security Bloggers Network syndicated blog from MojoAuth – Advanced Authentication & Identity Solutions authored by MojoAuth – Advanced Authentication & Identity Solutions. Read the original post at: https://mojoauth.com/blog/handwritten-passwords-touchscreen-devices

About Author

AndyC

Andy Curtis is an award-winning security consultant, researcher and public speaker. He has been working in the computer security industry since the early 1990s, having been employed by state and federal government, leading healthcare and banking providers across three continents. He has given talks about computer security for some of the world’s largest companies, worked with law enforcement agencies on investigations into hacking groups, and is a regular voice on TV and radio explaining IT security threats.

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