A NEW APPROACH TO ASSESS HIGH LEVEL PLANNING UNDERLYING COGNITIVE-MOTOR PERFORMANCE DURING COMPLEX ACTION SEQUENCES

While much work has examined low-level sensorimotor planning, only limited efforts have studied high-level motor planning processes underlying the cognitive-motor performance of complex action sequences. Such sequences can generally be successfully executed in a flexible manner and typically involve few constraints. In particular, no past study has examined the concurrent changes of high-level motor plans along with those of mental workload and confidence during practice of a novel complex action sequence. To address this gap, first a computational approach providing markers capturing performance dynamics of action sequences during practice had to be developed since past relevant works only employed fairly rough metrics. Such an approach should provide concise performance markers (e.g., distances, scalar) while still capturing accurately the changes of structure of high-level motor plans during the acquisition of novel complex action sequences. Thus, by adapting the Levenshtein distance (LD) and its operators to the motor domain, a computational approach was first proposed to assess in detail action sequences during an imitation practice task executed by various performers (humans, a humanoid robot) and with flexible success criteria. The results revealed that this approach i) could support accurately comparing the high-level plans generated between performers; ii) provides performance markers (LD, insertion operator) able to differentiate optimal (using a minimum of actions) from suboptimal (using more than a minimum of actions but still reaching the task goal) sequences; and iii) gives evidenced that the deletion operator is a marker of action sequence failure. This computational approach was then deployed to examine during practice the concurrent changes in high-level motor plans underlying action sequence execution with modulation of mental workload and an individual’s confidence in performing the task. The results revealed that as individuals practiced, performance improved (reduction of LD, insertion/substitution and movement time) while the level of mental workload and confidence decreased and increased, respectively. Also, by late practice the sequences were still suboptimal while being executed faster, possibly suggesting different dynamics between the generation of high-level motor plans and their execution. Overall, this work complements prior efforts to assess complex action sequences executed by humans and humanoid robots in the context of cognitive-motor practice, and it has the potential to inform not only human cognitive-motor mechanisms, but also human-robots interactions

Integration of Planning with Recognition for Responsive Interaction Using Classical Planners

Interaction between multiple agents requires some form of coordination and a level of mutual awareness. When computers and robots interact with people, they need to recognize human plans and react appropriately. Plan and goal recognition techniques have focused on identifying an agent's task given a sufficiently long action sequence. However, by the time the plan and/or goal are recognized, it may be too late for computing an interactive response. We propose an integration of planning with probabilistic recognition where each method uses intermediate results from the other as a guiding heuristic for recognition of the plan/goal in-progress as well as the interactive response. We show that, like the used recognition method, these interaction problems can be compiled into classical planning problems and solved using off-the-shelf methods. In addition to the methodology, this paper introduces problem categories for different forms of interaction, an evaluation metric for the benefits from the interaction, and extensions to the recognition algorithm that make its intermediate results more practical while the plan is in progress

Goal recognition and deception in path-planning

This thesis argues that investigation of goal recognition and deception in the much studied and well-understood context of path-planning reveals nuances to both problems that have previously gone unnoticed. Contemporary goal recognition systems rely on examination of multiple observations to calculate a probability distribution across goals. The first part of this thesis demonstrates that a distribution with identical rankings to current stateof-the-art can be achieved without any observations apart from a known starting point (such as a door or gate) and where the agent is now. It also presents a closed formula to calculate a radius around any goal of interest within which that goal is guaranteed to be the most probable, without having to calculate any actual probability values. In terms of deception, traditionally there are two strategies: dissimulation (hiding the true) and simulation (showing the false). The second part of this thesis shows that current state-of-the-art goal recognition systems do not cope well with dissimulation that does its work by ‘dazzling’ (i.e., obfuscating with hugely suboptimal plans). It presents an alternative, self-modulating formula that modifies its output when it encounters suboptimality, seeming to ‘know that it does not know’ instead of ‘keep changing its mind’. Deception is often regarded as a ‘yes, no’ proposition (either the target is deceived or they are not). Furthermore, intuitively, deceptive path-planning involves suboptimality and must, therefore, be expensive. This thesis, however, presents a model of deception for path-planning domains within which it is possible (a) to rank paths by their potential to deceive and (b) to generate deceptive paths that are ‘optimally deceptive’ (i.e., deceptive to the maximum extent at the lowest cost)