Our Mission
Turning robots intelligent, team-up with humans and other robots in an unstructured, dynamic environment by giving them imagination & ontology abilities.
Intelligent Robots
Robots have come a long way since their inception. Today, they are capable of executing basic tasks such as MOVE TO, while more advanced robots can even GRAB objects. However, despite these abilities, robots still lack intelligence.
Imagine a robot that can perform complex missions like "tidy up the kitchen" with ease. Such an intelligent robot would possess prior knowledge ontology and human-like imagination, enabling it to handle hierarchical planning and execute its mission even in an unfamiliar environment.
This is just the beginning of what intelligent robots can achieve. With the potential to learn, adapt, and evolve, the possibilities are endless.
"The true sign of intelligence is not knowledge but imagination" < Albert Einstein >
Founded in 2017, we enable complex multi-agent auto-missions with or without humans in the loop. Our field proven solutions handle multi-agent and multi-action complex missions, taking into account agents common-mission goals.
In order to achieve full autonomy and support real-life mission-critical tasks we use a unique combination of model-free deep-reinforcement-learning and model-based planning. Planning & learning algorithms require fast and accurate digital-twin simulators.
Our Trained-By-Simulator and Sim-2-Real-Simulator support acceleration of more than 1000X, enabling cost effective training of multi-agent systems with reduced time-to-market and low-latency real-time planning.
Our deep-reasoning inference engines use various reasoning algorithms, for deduction, induction, abduction and more, are based on large scale and low latency ontology instances.
Multi-Agent Hierarchical Planning
Our hierarchical planning AiGENTs increase autonomous systems efficiency while operating as a team with or without humans in the loop. Hierarchical planning, which mimics the human brain decision making process, is designed to function in real-life scenarios. Like humans, hierarchical planning AiGENTs should function under uncertainty, ambiguity, partial observations, dynamic environment and sometimes even under wrong perception inputs (the most challenging cases). Supporting real-time applications, our deep-planning AiGENTs are capable to construct and execute plans for very complex missions, using low latency deep-planning algorithms.
Last, meeting our customers requirements, we provide an “explainable AI” solution giving high confidence level to human supervisors.
Ontology Based Reasoning
Situation awareness & understanding is the ability to estimate and predict a possible situation involving multiple actors and/or objects in different locations, that may trigger events or activities occurring over time, and where the meaning of the situation is revealed by integrating previous knowledge with evidence from multiple sources.
We combine high-order logic, knowledge-graphs, stochastic-modelling, Bayesian-inference and model-based deep reinforcement learning framework to provide our customers "better than human" ontology based deep reasoning AiGENTs.
On Premise LLM for Robots
In human-robot interactions, mapping natural language to formal language is crucial for effective communication. LLMs and transformers help bridge this gap by interpreting and translating human instructions, expressed in natural language, into formal language that robots can understand and execute. This process involves extracting essential information, understanding context, and converting instructions into actionable commands, enabling robots to perform tasks efficiently and interact seamlessly with humans.
However, while these tools offer significant benefits for enhancing robot intelligence, they can sometimes generate inaccurate or hallucinated information.
At AiGENT-TECH, we seamlessly integrate LLMs with our ontology-based reasoning and planning mechanisms. To ensure our robots consistently demonstrate appropriate and ethical actions, we employ logical controllers that govern their behaviour and decision-making.