of Computer Science, Turin, Italy bICAR-CNR, Palermo, Italy cUniversity of Turin, Dept. insects), which can collectively produce optimal solutions. Sorry, preview is currently unavailable. Beyond Subgoaling: A Dynamic Knowledge Generation Framework for Creative Problem Solving in Cognitive Architectures ... a replanning strategy is required in order. Below we describe the computational approach and successively we validate it in two computational experiments, which focus on two issues: (i) under which conditions using subgoals leads to optimal behaviour and (ii) whether the specific subgoaling mechanisms envisaged here can be linked to neuronal computations in primates. Require: Agent's initial state s0, goal state sgoal, subgoal algorithmic priors p(SG), maximum number of forward inferences Tmax. In the first (without-subgoals) strategy, the probability of choosing a subgoal different from the goal state is zero: p(SG ≠ sgoal) = 0. answer choices . Overall, these results highlight that the proposed method using subgoals is more efficient compared to a standard planning-as-inference procedure that does not use subgoals and makes a more parsimonious use of resources (e.g. Expectancy‐driven strategies enable learners to actively process instructional materials by predicting the next step in a task. Performance in the four-rooms scenario. By mapping the program (si, πj, si′) into a binary string with length , we can algorithmically assign to it an a priori probability equal to . The average percentage of instances that fail to find a successful planning strategy (for N = 100) is shown in figure 3b, revealing again an advantage of the strategy with-subgoals (dotted black line) over the strategy without-subgoals (grey line), especially in the first inferential steps. To compare the inference with and without subgoals, we consider various factors: the number of successfully reached goals, the optimality of behaviour, expressed here as the number of inferential instances achieving the optimal planning strategy (in this scenario, a sequence of eight states, including both start and goal states), the complexity of the inference (i.e. the percentage of instances failing to arrive at the goal) and the complexity of the control (i.e. The probability values are: 0.03 for S7, S12, 0.04 for S1, S4, S5, S9, S10, S14, S15, S18, 0.06 for S6, S8, S11, S13 and 0.08 for S2, S3, S16, S17. a specific policy associating with every state a ‘rest’ action ɛ) is selected determining st+1 = st = sgt. Every path c corresponds intuitively to a distinct program , but each program can be attained by different policies. extract probabilistically) candidate subgoals from the previously described a priori subgoal distribution (see §2.2); the candidate subgoals are then retained or discarded by considering the computational complexity of the resulting sub-problems or programs. S probability distribution at each time step (numbered from a to d) for the monkey path-planning task reported in [7]. a … These experiments revealed that during the preparatory period monkey lateral prefrontal cortex (lPFC) neurons encoded sequential representations of the path plans [7] and that these prefrontal representations are specific for goals and not motor actions (i.e. A key distinction in the proposed approach is between potentially useful subgoals and selected subgoals, the former being a priori and independent of the agent's current goal and the latter dependent on it. You can’t solve all of the world’s problems, but you can solve many of the world’s problems. Graphical model (DBN [31]) used for the simulations. During control, subgoals permit maintaining the smallest possible information in working memory that is sufficient for successful task achievement [35]. Importantly, here we use considerations of algorithmic probability to select subgoals and policies; as a consequence, the plan results in series of transitions across states with highest algorithmic probability. To guide subgoaling towards the final goal state (rather than, say, away from it), not only the inference ‘clamps’ the just-achieved subgoal as the current state, but it also assumes that ft+1 = 2, that is, it fictively assumes that the goal state is (has to be) observed. Running inferences based on such prior information would generate a well-known dilemma between faster but less flexible (habitual) and slower but more flexible (goal-directed) selection mechanisms [26,49–53]. The grey scales indicate the SG a priori probability distribution for the task: S1, S3, S4, S8, S14, S18, S19, S21 has probability 0.045, S11 has probability 0.048, S6, S10, S12, S16 has probability 0.0485, S2, S9, S13, S20 has probability 0.0486 and S5, S7, S15, S17 has probability 0.05.Download figureOpen in new tabDownload PowerPoint. C)not create fewer than ten subgoals. Figure 6. In machine learning, pre-occupation with free-standing performance has led to comparative neglect of this resource, illustrated under the following headings. Probability distribution of SG in the four-rooms scenario, calculated by considering the average of 100 instances at each time step (numbered from a to h).Download figureOpen in new tabDownload PowerPoint. Results are shown for different number of independent inferential instances (50, 100 and 1000). Here the results are relative to the programs used to plan (up to the next subgoal) at each inferential step, for N = 100 instances and averaged on 10 runs. subgoal distributions and states that compose optimal plans) and how these neuronal responses might be produced (i.e. In the subgoaling strategy, learners are prompted to group logical steps with a procedure into meaningful subgoals that encourage learners to self‐explain the logic of these steps. We validate the proposed method using a standard reinforcement learning benchmark (four-rooms scenario) and show that the proposed method requires less inferential steps and permits selecting more compact control programs compared to an equivalent procedure without subgoaling. the average length of the programs necessary to achieve the task). work backward in you planning; first creating a subgoal that is closest to the final goal, and then work backward to the subgoal that is closest to the beginning of the problem-solving effort They are. from S11 to S3) is shown in figure 8. In other words, planning consists of selecting actions that bias the probability of future events towards desirable states. We set the prior probability S18 to be the highest among all the values of p(SG) (table 1). In the spirit of Algorithmic Probability Theory introduced by Solomonoff [23,34], we assume that (in a discrete state space) it is possible to determine a set of executable instructions by considering a starting state si, an arrival state si′ and a policy πj. Following Occam's razor, we propose that good subgoals are those that permit planning solutions and controlling behaviour using less information resources, thus yielding parsimony in inference and control. However, it is possible to introduce various approximations and heuristics, such as that presented in appendix B, to render their evaluation computationally treatable. Given two states, si and si′, we exploit a standard depth-first search algorithm for figuring out all the paths between them. The free-energy principle: a unified brain theory? recognizing problems involves. A second example is the implementation of an opportunistic scheduling strategy where the partial solution of a high-level problem is used to focus the system on low-level activities required to solve the remainder of the problem (focus of attention through subgoaling). An integrative theory of prefrontal cortex function, Thinking as the control of imagination: a conceptual framework for goal-directed systems, Activity in the lateral prefrontal cortex reflects multiple steps of future events in action plans, Representation of immediate and final behavioral goals in the monkey prefrontal cortex during an instructed delay period, Hierarchical models of behavior and prefrontal function, Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning, Recent advances in hierarchical reinforcement learning, Hierarchically organized behavior and its neural foundations: a reinforcement learning perspective, Hierarchical behaviours: getting the most bang for your bit, Hierarchical solution of Markov decision processes using macro-actions, San Francisco, CA: Morgan Kaufmann Publishers Inc, Width and serialization of classical planning problems, Programming in the brain: a neural network theoretical framework, Discovering neural nets with low Kolmogorov complexity and high generalization capability, Performance in planning: processes, requirements, and errors, Subgoal length versus full solution length in predicting Tower of Hanoi problem-solving performance, Neural correlates of forward planning in a spatial decision task in humans, Society for Artificial Intelligence and Statistics, The mixed instrumental controller: using value of information to combine habitual choice and mental simulation, Probabilistic inference for solving discrete and continuous state Markov decision processes, Goal-directed decision making in prefrontal cortex: a computational framework, Goal-directed decision making as probabilistic inference: a computational framework and potential neural correlates.
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