Systems and methods for understanding (inputing meaning to) multi model data streams may be used in intelligent surveillance and allow a) real-time integration of streaming data from video, audio, infrared and other sensors; b) processing of the results of such integration to obtain understanding of the situation as it unfolds; c) assessing the level of threat inherent in the situation; and d) generating of warning advisories delivered to appropriate recipients as necessary for mitigating the threat. The system generates understanding of the system by creating and manipulating models of the situation as it unfolds. The creation and manipulation involve “neuronal packets” formed in mutually constraining associative networks of four basic types. The process is thermodynamically driven, striving to produce a minimal number of maximally stable models. Obtaining such models is experienced as grasping, or understanding the input steam (objects, their relations and the flow of changes).
Please help me write a proper abstract based on the patent claims. CLAIM: 1-25. (canceled) 26. A computer implemented method of robotic control which utilizes a pool of artificial neurons and procedures operating on the pool, comprising the steps of: a) interacting the pool with data streams generated by sensors or other sources data which are exposed to an environment; b) self-partitioning into quasi-stable neuronal packets and capturing persistent data groupings in the data streams; c) constructing situation models where objects in the environment are represented by neuronal packets, wherein behavior of the objects over time is represented by variations in packet activation patterns, and wherein relations between the objects are represented by coordination in the variation of activation patterns in the corresponding packets; d) executing situation models constructed in the constructing step to simulate a robot's manipulations of the objects and consequences of the robot's manipulations; e) applying results of executing in the executing step so as to optimize action plans in performing human-defined missions; and f) applying the optimized action plans to generate motor activities applied to modify objects in the environment and inter-object relations. 27. The method of claim 26, further comprising the step of recursively combining simple situation models into progressively more complex models by: a) constructing simple three-partite situation models comprising pairs of objects and relations between them, b) representing the simple three-partite situation models as discrete entities or units, with response vectors computed on the discrete entities or units, and c) combining the discrete entities or units into more complex models and subsequently representing the more complex models as macro units associated with macro unit response vectors. 28. The method of claim 26 further comprising: defining procedures to compute properties of the objects; and using the computed properties to streamline construction of the situation models by a) representing variations in packet activation patterns as distinct trajectories of response vectors computed on the packets, b) establishing, for every packet, admissible ranges of packet vector movements as movement affordances, c) identifying movement affordances with properties of one or more corresponding objects of the objects, and d) defining rules for constraining model construction depending on the properties of the objects participating in a situation. 29. The method of claim 28 wherein the rules for constraining model construction include prohibiting models containing objects with incompatible properties. 30. The method of claim 26, further comprising defining procedures for inferring causes of changes detected in objects and predicting future changes by a) constructing at least one of the situation models to comprise entities A and B and relation C, b) executing the at least one of the situation models in the forward direction, by varying trajectories of a packet vector in the entity A and assessing resulting variations in trajectories of a packet vector in the entity B, and c) executing the at least one of the situation models in the reverse direction, by defining a subset of packet vector trajectories in entity A admissible under detected trajectories of the packet vector in the entity B and the relation C, selecting a trajectory in the entity A that coordinates with the detected trajectory in the entity B, and defining corresponding changes in the entity A as a cause of the detected changes in the entity B. 31. The method of claim 30 further comprising defining procedures for elucidating the situational models and generating human-understandable explanations of inferences and predictions derived from executing the situational models by a) attaching symbolic entities or labels to packets or objects, distinct trajectories of packet response vectors or properties, and forms of trajectory coordination or inter-object relations, and b) using one or more of the symbolic entities or labels, distinct trajectors of packet response vectors or properties, and forms of trajectory coordination or inter-object relations to construct propositions expressing inferences and predictions in terms of the objects, their properties and inter-object relations. 32. The method of claim 31 further comprising defining procedures for generating explanations in the form of nested propositional structures by recursively applying the operation of enfolding to groups of symbolic entities or labels and attaching symbolic entities or labels to the outputs of such recursive enfolding. 33. The method of claim 32 further comprising defining procedures for generating explanations in the form of propositions by recursively applying the operation of unfolding to nested propositional structures. 34. The method of claim 32 further comprising defining procedures for establishing rules or syntax for combining symbolic entities or labels into structures and applying such rules to constrain generation of nested propositainal structures. 35. The method of claim 31 further comprising constructing recursively progressively more complex explanations by a) constructing low-level propositions corresponding to simple tri-partite situation models, b) treating the simple propositions as units, and c) combining the units into higher-level propositions corresponding to the higher-level models. 36. The method of claim 31 wherein construction of the situation models includes applying rules of propositional logic to reason about situations which includes abductive reasoning involving construction of simple models of situations manifested in persistent data groupings in the data stream. 37. The method of claim 26, further comprising: exposing a cognitive controller to streams of data generated by sensors placed inside a robot; constructing models of internal situations to the robot; applying the constructed models to infer causes of the internal situations or predict an impact of the internal situations on the robot's performance in carrying out one or more human-defined missions; and d) using results of the applying step to construct action plans. 38. The method of claim 37 wherein the internal situations involve component malfunctions. 39. The method of claim 37 wherein the applying step infers causes of malfunctioning of one or more components. 40. The method of claim 31, further comprising defining procedures to allow a robot to utilize human natural language feedback concerning the situational models and their execution, with the human natural language feedback, including one or more restrictions on the admissible properties of the objects and their relations in the situation model by transforming the human natural language feedback into operations on the situational models to adjust the composition of the neuronal packets, envelopes of admissible packet vector trajectories, and admissible forms of trajectory coordination. 41. The method of claim 40 further comprising defining collaboration between multiple robots and one or more humans by a) defining procedures for exchanging data between one or more of the multiple robots and the one or more humans, b) constructing the situation models by individual robots of the multiple robots and generating explanations in a human-understandable form, c) reconciling the individual models of the situation models, and formulating a common situational model shared by all of the multiple robots and the one or more humans that are collaborating.