Turning robots to be intelligent, team-up with humans and other robots in an unstructured, dynamic environment by giving them imagination & ontology abilities.
Intelligence Brings More Value
- Multi-agent teaming operating-system for unstructured environment missions.
- Supports first-time-seen, unstructured environments.
- Human-Machine & Machine-Machine teaming capabilities.
- Supports amorphic complex teaming missions.
- Scalable solution for massive multi-agent deployments.
- Supports interoperability between any 3rd party heterogenous robots.
- Imagination-engine optimizing mission’s goal score in an unstructured environment.
- Deep-reasoner engine overcoming real-life partial observability and uncertainty.
- Low latency response time supporting real-life dynamic environments.
- Supports new use cases, new functionality within days.
- Low-cost training OPEX based on dedicated low fidelity simulators.
"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.
The AiGENT-TECH team has a very rich track record of past successes in developing and deploying complex technology products.
With strong theoretical AI background & proven engineering know-how we create the smartest digital workforce of tomorrow