The document discusses techniques for developing cognitive skills in robots to allow them to interact with humans. It describes using a combination of conceptual categories and RDF triples stored in an ORO knowledge server to represent a robot's knowledge. The robot uses various modules like SPARK for spatial reasoning and situation assessment to perceive objects, and gains knowledge through symbolic reasoning, theory of mind modeling, and a working memory model. Key techniques discussed include using OpenCyC concepts to structure a robot's commonsense knowledge and allowing it to reason about objects from different perspectives.

1. Prerequisites of AI Techniques making Robot to perform task with
human.
Key decisional issues required for the communication or interactive robot to successfully move free
in a given space and perform tasks with human.
Skills required for AI interaction with human: On situation based abstract knowledge as much as
possible permutations and combinations of analysis to be applied the outcome is acquired and
represented as thinking models for multiple robots and humans to make the robots to identify the
human multi dialogues, task planning and human robot joint task performance.
Response to AI requires integration: Modelling of Human knowledge; acquiring, representing,
changing in a traceable way this is called abstract knowledge applying reasoning to this knowledge
to make decisions into human physical actions both communicate to and in coordination with
humans.
Brief About AI Techniques
AI techniques are given authority to act in a certain way from visual identification to symbol
Identification, simplify the task planning to perform to build an understandable theory brain
modelling that stimulate to actions recognition and learning.
Constructing a virtual intentional layer.
Using reliable multiple independent software modules to build a logical robot architecture is not
only the challenge but also robotic structure and design challenge, challenge to manage and
capturing of logical human interactions is also raises an issue while building virtual intentional layer
Our basic assumption is human interactions are very easy to achieve If the robot virtual brain is rely
on internal logical human interactions i.e. robot virtual brain must be built with logical human
interactions. We implement this principle by purely depending on acquiring external human
knowledge, representation and manipulation: Software components interact each other with logic
using Meta logic and whose logics are manipulated by humans, this type of principle is also
applicable at high level architectures a group of known robotic layered architectures.
Detail internal study of conscious layer
As of now we learn that logic manipulated by humans to build a robotic layered architecture that
means the software components interactions should be bidirectional. This article suggest that not to
introduce new ideas among the sub layers of conscious components.
The conscious layer composed of six main modules interacts with a low level sensory layer as shown
in the below diagram

2. Conscious diagram consists of 6 main modules communicates with low level sensori- motor layer,
knowledge is managed in as logical black board, pictured below with thick black board(Symbolic
facts and beliefs management). Most of the data streams are symbolic statements exchanged
through this logical blackboard.
Our architecture is the BDI architecture Beliefs, Desires, Intentions BDI architectures points to gain
knowledge through practical i.e. deciding, planning and to perform the task to reach the goal. The
centre of attraction of BDI architecture is task, plan, representation and execution is purely central
and automatically chooses the sub task to reach the sub goal. For any mental process in gaining
knowledge and actions is central to our approach as well involves the interaction between two
software components module to acquire desires from other agents, the planner and the execution
controller is the decision maker to consider the desire or not, if the planner and execution controller
considered a particular desire that particular desire is now incoming desire as a goal and then these
goals are managed as intentions from these goals through symbolic task planner. We extend the BDI
architecture explicitly that is desires are triggered from outside according to BDI. Situation
assessment, action monitoring, and processing of vital importance speech is the major focus in BDI
architecture.
Knowledge Model
In our BDI architecture knowledge change is dependent on central server called ORO server which
stores huge knowledge produced by related components called clients to for interaction between
ORO server and its related clients Jason Based RPC API to query the knowledge base. For our
understanding the knowledge is considered as RDF triples as per the OWL sub- language in BDI
architecture. Another knowledge model is cast model that is knowledge is forcefully spread widely

3. throughout an area or group of people. Cram model says that knowledge base is an active storage
location gain control over the knowledge or gain knowledge by asking queries to the perceptual
components. BDI architecture has great observability as well as high modularity means able to
communicate effectively to the components through uniform and clear or detail API. Due to BDI
architecture the experienced knowledge or priori knowledge is stored in a hub called metaphysics
dealing with nature of being and loaded into robot brain at start up. The instance i.e. priori
knowledge reacts to the experience knowledge and surprisingly implements common-sense
knowledge of the robot and might optionally include situation based specific knowledge. The second
part of the knowledge is implemented while communicating and planning in this process the third
part of the knowledge is implemented in the form of symbolic statements knowrob eg: Object1 is on
Object 2. Knowrob means the symbolic knowledge base of robots.
Why robots uses knowrob to communicate?
Knowrob is the robots understandable knowledge calls a reasoning queries while communicate to
respective human being or components. Knowrob design is very scalable and clear at any time to
spread the knowledge to communicate uniformly to diversified entities, components or human
beings. Knowrob uses spatial relationship i.e. identifying the objects like ours to understand the
object characteristics for successful spatial relationship priori based approach continuously runs in
the background.
Advantages of RDF triples that ORO Server Relies on
RDF is an art of logics to communicate with ORO Server, ORO server always relies on the detail
instructions of logics called OWL to represent and manipulate knowledge. Advantages of RDF Triples
and detail instructions logics such as good understanding in decision making, and RDF Triples are
widespread in web community and availability of advance libraries to manipulate metaphysics which
runs background of the robot. The difficulty in RDF is this RDF uses binary sentences acts the means
to express with the system and binary sentences are complex idea. Alternative to binary is proposed
as knowrob inserts the RDF with more expressive schematics like “Prolog” (Reasoning statements)
other limitations like closed world reasoning.
RDF Triples with binary has classification performance issue means it uses about 100 classes and 200
instances which leads to notable delays during interactions. To overcome this issues along with
Prolog/OWL combination relies upon by Knowrob, answer set programming has been used in
robotics for instance for better function reasoning that never decrease or increase.
Epistamic logics means validation logics are developed in such a way relevant to specific field of
social human –robot interactions. Epistamic logics or validation logics allow to represent alternative
mental models. For first experiment in metaphics OWL Concepts and Categories in a subject area
proved simple, effective and affordable symbolic scaffolding structure of our experimental
applications.
The summary is because of A set of Concepts and Categories in a subject area called Ontology and
RDF Framework proved simple compared to full logical languages like Prolog logics or Modal logics,
A set of concepts and categories and RDF statements adoption has also effectively helped to grow
awareness amongst colleagues to build new components.
Note: RDF Means Resource Definition Framework representing information in the form of web.
The OpenRobots with A set of Concepts and Categories in a subject area
As discussed earlier combination of RDF frame work and concepts of categories in a subject area
logics are exchanged between the components are organized systematically within the ORO server
Or knowledge.

4. OpenRobots common sense conceptualization is statically forcefully inserted into part of concepts of
categories in a subject area. Common sense insertion into robot ORO server is dependent on two
requirement i.e. practical that to compatible to existing standards.
The figure shows the ORO commons sense conceptualization the concepts are shared with OPEN
CyC foundation ontology comprises of sense of information science which contains general items
such as
Object, Property, relation. In above figure class names are represented to the OPENCYC name space.
What is OPEN CyC?
CyC meaning is very large, interconnection of knowledge base ideas, and inference engine,
conclusion engine or inference engine which is developed by MCC Austin MCC derived as
Microelectronics and Computer Science Corporation in Ausitn, texas during 1980’s. CyC team was
lead by Doug Lenat, have added set of facts, rules of thumb and heuristic method i.e. shortcuts to
produce good-enough solutions given a limited timeframe or deadline. CyC is an attempt to do
symbolic AI on a large scale. Cyc is purely depends on logical assertions. CyC currently contains
approximately 400000 significant facts of statements all are simple statements these simple
statements of facts, rules will derive conclusions if the set of rules or facts are satisfied. The
conclusion engine characterised by reasoning. With the help of CyC innovative applications are
evolved in the areas of diverse in database browsing and integration, getting back captioned image
something back somewhere or captioned image retrieval and natural language processing. In
January a new independent company named Cycorp was created to continue CyC project and
president for this company is Doug Lenat is located in Austin and supported by Apple, Bellcore, DEC,
DoD, Interval, Kodak and Microsoft.
The summary of Open Robots with A set of Concepts and Categories in a subject area is all about
conceptualization, logics and perception of robots how the robots reacts or capture the object and
what characteristics that robot derives from the object is as shown below picture.

5. Subclasses of Partially Tangible Thing explicitly stated in the Open Robots with A set of Concepts and
Categories in a subject area. In the above diagram the given object to the robot is glass, even though
the object is glass the robot derives more information about the object and if human says to robot
that the given object is car the robot uses common sense and screens the subclasses of the object
I.e. size, solid type, shape of the object, what can be filled in the object and deliver the dialogue the
given object is glass and denies the human assumption.
Mental processes involved in gaining knowledge and comprehension called
Cognitive skill
To develop cognitive skill the component should recall the experience to perform this the
component should keep track of previous states is typically important to perform correctly. The
cognitive skill or capabilities is already implemented in the ORO knowledge base itself, but we need
to know more detail about situation handling module called spark, the dialogue processor module
called dialogues, the symbolic task planner called HATP module and lastly the main feature of our
execution controllers SHARPY and PyRobots.
Internal mental process involved in gaining knowledge and comprehension
We call above mentioned modules in internal mental process capabilities are tightly bound with
knowledge model, and hence implemented directly within the ORO Server. In the internal mental
processing we present three of them they are Symbolic reasoning, theory of mind modelling and our
origin of memory management we study these three theories in details as below.
Symbolic Reasoning.
Symbolic reasoning is act as the reasoner to reason on the knowledge base. It supports various
standard conclusion based mechanisms they are consistency checking, concept satisfiability,
classification and realisation. In case of inconsistency reasoner can also provide explanations besides
reasoner module ORO server implements various algorithms to identify similarities and difference
between classes and instances. The common ancestors algorithm useful to determine the most
specific classes that include a given set of individuals. With the help of ancestor algorithm it is very
easy to understand the most generic types to differentiate two concepts they are clarification and
discrimination algorithms that play a key role in the process of interactive technique of the
semantics of concepts. Clarification and discrimination algorithms derive the characteristics of
individual objects such as size, shape, type of material asserted in the common sense acquired by
robot through perception or pro-active questioning or derived from other reasoning algorithms like
the common ancestors and different ancestors. The distinguishing feature algorithm consists then in
looking for distinguishing features that allow maximum distinguishing features among set of
individuals.

6. Theory of Mind
Theory of mind is the mental process of perception ability that allows a subject to represent mental
state of another component or agent, possibly including knowledge that a combination of
statements, ideas or features which are opposed to one another. For example for an agent a glass is
visible but same glass object is not visible to agent B this is called contradiction between agents.
From the interactive robots it is defined the robots store, retrieve the information, beliefs of the
humans it interacts with. In the same way the robots tends to responds to the new human agent if
the robots create new independent knowledge models for each human interactions then only
human robot interactions is successful. All the concepts and categories in a subject area shares a
common sense knowledge but reply on the robot assumption of each agent’s perspective for their
actual representation. For example in the robot make calculation and assumes the given “glass is
visible trues” while the human model is updated with “glass is invisible false” these two logical
statements are likely the same but differ when kept side by side. The two knowledge models are
implemented with two different concepts and categories in a subject area, the difference in
characteristics and features does not appear and both the models remain logically act in the same
way so as to be fair or accurate. The above fair logical statements can be considered for false belief
experiment to explain this experiment in brief let us consider child A and child B. Child A and child B
hides an object when return A we ask the child where can the object here we need to understand
that children is not separated from the world i.e A still thinks that the object is in original position
since A does not know B hidden it in new place, depending on these individual models we can apply
the same experiment on robots.
Working Memory Model
Working memory model states about short term and long term memory. This short term and long
term memory concept is described in GLAIR the process of knowing or perceiving Architecture.
Unlike GLAIR architecture SOAR process of knowing or perceiving architecture try to reproduce
human like memory organization. Commonly memory is associated with the process of forgetting
facts after a variable amount of time, it actually covers more machine parts of robots i.e. the action
learning or pre-activation triggred by specific context.
The ORO server features a machine parts imitate to minimalistic to memory families. When new
statements are inserted in the knowledge base, a memory file is attached to those machine parts.
Three such files are established in advance Short term, consisting of a series of separate parts, long
term. The parts are attached to duration of time lines 5 minutes or 10 minutes after this duration
the statements are automatically removed this approach is no longer prolonged to next stage. In the
sense the consisting of series of separate parts should respond to the series of logical statements
i.e. we expect the spare part respond to the event rather than respond to life span. But in
sometimes we use life span concept to certain parts of the robots to make this concept active we
use language module where the human interactions mark or logical statements are considered as
active concepts by robot for example if human says strike all yellow color balloons, all yellow color
strike balloons are “Active Concepts” by inserting statements as “human-active concepts” in the
short term memory. Active Concepts feature during dialogue interpretation cannot be resolved to
access concepts referred to. In other hand our perception layer does not make use of Active
Concepts mechanism.
Geometric situation assessment module SPARK
SPARK computes serving as a symbol relationship between objects and agents and send them into
knowledge base.
Capturing and coordinating knowledge in the physical world.

7. Coordinating perceptions serving as symbol requires perception abilities and their serving as symbol
interpretation. We call physical situation assessment the process of knowing the skill of the robot
exhibits when it assess and nature of its environmental conditions, monitors it’s development of the
things surrounded it to achieve this we use SPARK module spatial reasoning and knowledge. SPARK
acts as the reasoner and generates serves as a symbol (symbolic) knowledge from the geometric of
the environment with respect to relations between objects, robots and human and also take
different perspective that each agent has on the natural world. Geometric model of the nature
world that serves both as the process of perception modalities and as bridge with the serves as a
symbol (symbolic) layer. The geometric model is constructed from 3D CAD models of the objects,
furniture, robots, and full body, sailing models of human it is updated at run-time by the robot’s
sensors. Spark runs without interruptions and updates the knowledge base at about 10 Hz.
To be Continued…..