A stimulus may be defined as a class of relationships which exists between two parties (a sender and a receiver), specifically where the sender initiates the relationship and the receiver is primarily subject to it. Circumstance may then be defined as a particular response to a stimulus, and a situation as a set of coordinated circumstances that can characterise behaviour.
This leads to defining observation as the witnessing or collection of circumstances and situations.
In this model, a circumstance is analogous to a stimulus-response pair or S-R link, and a situation may be defined by multiple, sequenced or simultaneous circumstances.
With this model, it is easy to define a known stimulus, circumstance and situation by identifying the sender, receiver, response, and the pattern that they occur in. However, is it possible to define yet-to-be-known circumstances and situations purely from observations and then simulate this as behaviour in artificial entities by producing both known and learnt (previously unknown) circumstances and situations?
Circumstance: Contextual Situational Information
Circumstances can be very rich, full of interrelated properties and co-existing facts. Kurt Lewin suggested through his Field theory that situational context, i.e the environmental or ‘space’ influence, contributes significantly to understanding behaviour that occurs within it (Burnes and Cooke, 2013).
This suggests that experiential learning should be based on more than a collection of S-R associations and that other contextual circumstantial information about the environment and situation is important. Arguably richer S-R links might be gathered in context, which can be provided by environmental simulations.
It is in this way that a simulation environment that elicits notions of perceptual reality to an inhabiting agent may assist in providing the surrounding context in which S-R links are experienced. This situational context can then be used to help define, identify collections of discernible situational circumstances in order to better understand experiences within it and identify situational boundaries. This may require the formation of a model such as that presented in the previous section for defining circumstances (and also storing and querying it).
Modelling and defining circumstances might be achieved by collecting what this research defines as circumstantial situational information or CSIs, i.e contextual information from the environment or constituents of situations. This, for example, might be stored and then be used as a lookup query along with any sensory-based knowledge when forming new or identifying existing situational knowledge.
A primary consideration would need to be spent determining how much and which information is collected, and what would constitute or affect what one defines as being relevant (and unique) when discerning boundaries, particularly in real-time.
Indeed, failures derived from the inability to reliably identify and establish relevant context are a principal concern for safety, reliance and trust in artificial intelligence (Denning and Arquilla, 2022).
Despite the many theories of knowledge, situational knowledge formation is likely to result from a latent knowledge-formation process (probably systematic, albeit complex) from which an understanding of any situational circumstance can be derived. Such resultant understanding is likely to be the generalised deconstruction of dynamic and complex situational stimuli and circumstances into discernible situational boundaries from which actionable situational rules/knowledge can be created.
A possibility, too, is the ability to create behavioural rules that are based on observational experiences. These rules, being based on observation-based training, could develop rules that are less fragile as they are less dependent on specific contexts having been derived from generations of generalisation informed by many variances experienced during explorations. This might be considered a form of dynamic training based on varying experiences as opposed to static training data.
Generalization
As part of the Little Albert experiment performed by J.B Watson, it was shown that a generalised stimulus-response association (learning) is established such that the perception of similar stimuli (but not the same) produced the same learnt response in Little Albert. In the experiment, the same response was produced when either the white rat (subject of the experiment) or other, “…white furry objects such as dogs, rabbits, fur coats, cotton, and wool…” were perceived, suggesting that generalisation of detected stimuli is a fundamental characteristic, even in infants (Guercio, 2018).
This suggests that a learned response could happen when circumstances are similar but not the same as the original situation, i.e the context is similar. This has implications in any potential software model that looks up circumstantial information to determine its next situational response, as it would need to look beyond the specific and detect similar or general, i.e, fuzzier cases that derive from it.
Generally, the problem of context is an open concern and is particularly relevant in light of failures found in even contemporary AI such as Artificial Neural Networks (ANNs) where research shows that, “…a frequent feature of these failures is the machines were being used in a context different from what they were designed or trained for”, often because they are unable to generalize and are based on fixed signatures (Denning and Arquilla, 2022).
Relevant questions posed by that research are, “…can the machine detect there has been a change in the environment…”, and, “…can a machine read context in real time and adapt its recommendations?”, and also highlighting the ever-present need to be able to, “…tell when a rule is relevant to the situation at hand”. This evidently highlights the importance of change detection within environmental situations. These, it argues, are inherently difficult questions that AI has been trying to solve ever since its inception.
A possible approach to generalising experiences is by storing or processing not only S-R links, but also any associated contextual situational information (CSI) that identifies circumstances in observed situations. The retrieval mechanism for such a model would need to be able to find circumstantial data that is generalizable to that specific circumstantial information, in the same way infants do. Efficiency is likely to be a factor of success.
While contemporary machine learning approaches based on the provision of static training data, and therefore the inherent possibility and oftentimes likelihood of specific training data that has a limited context, i.e bias, our proposed research explores how training data that can by synthesized by exploration of the domain in which it is relevant, by observing variances and similarities in it, might help to gain a richer contextual and generalized understanding to base its learning on. This effectively means dynamically creating training data by experiencing more variations of its domain. This could allow for a better approximation of algorithms or rules that can be used to describe it. This in itself is not novel (exploration), but reasoning about the effects of exploration could be.
References
Burnes, B. and Cooke, B. (2013) ‘Kurt Lewin’s Field Theory: A Review and Re-evaluation’, International journal of management reviews : IJMR, 15(4), pp. 408–425. doi: 10.1111/j.1468-2370.2012.00348.x.
Denning, P. J. and Arquilla, J. (2022) ‘The context problem in artificial intelligence’, Communications of the ACM, 65(12), pp. 18–21. doi: 10.1145/3567605.
Guercio, J. M. (2018) ‘The Importance of a Deeper Knowledge of the History and Theoretical Foundations of Behavior Analysis: 1863-1960’, Behavior analysis (Washington, D.C.), 18(1), pp. 4–15. doi: 10.1037/bar0000123.